From bb7f5696ef576bf635a25548d354426fc12c3eb4 Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Mon, 14 Apr 2025 22:13:23 +0800 Subject: [PATCH 001/474] Add aligned-spin conditions to PhenomPv2 angle calculations --- src/ripplegw/waveforms/IMRPhenomPv2_utils.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py index 42aac9ae..fc680b93 100644 --- a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py @@ -16,6 +16,8 @@ from ..typing import Array from .IMRPhenomD_QNMdata import QNMData_a, QNMData_fRD, QNMData_fdamp +MAX_TOL_ATAN = 1.0e-15 + # helper functions for LALtoPhenomP: def ROTATEZ(angle, x, y, z): @@ -92,7 +94,12 @@ def convert_spins( thetaJ_sf = jnp.arccos(J0z_sf / J0) - phiJ_sf = jnp.arctan2(J0y_sf, J0x_sf) + no_inplane_J0 = jnp.abs(J0x_sf) < MAX_TOL_ATAN and jnp.abs(J0y_sf) < MAX_TOL_ATAN + phiJ_sf = jax.where( + no_inplane_J0, + jnp.pi / 2.0 - phiRef, # This is the aligned-spin case + jnp.arctan2(J0y_sf, J0x_sf), + ) phi_aligned = -phiJ_sf @@ -117,7 +124,12 @@ def convert_spins( tmp_x, tmp_y, tmp_z = ROTATEY(-thetaJ_sf, tmp_x, tmp_y, tmp_z) tmp_x, tmp_y, tmp_z = ROTATEZ(kappa, tmp_x, tmp_y, tmp_z) - alpha0 = jnp.arctan2(tmp_y, tmp_x) + no_inplane_LN = jnp.abs(tmp_x) < MAX_TOL_ATAN and jnp.abs(tmp_y) < MAX_TOL_ATAN + alpha0 = jnp.where( + no_inplane_LN, + jnp.pi, # This is the aligned-spin case + jnp.arctan2(tmp_y, tmp_x), + ) # Finally we determine thetaJ, by rotating N tmp_x, tmp_y, tmp_z = Nx_sf, Ny_sf, Nz_sf From d3ed2c0171f68fa5be596e2cbc5fc4cd5e90f023 Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Tue, 15 Apr 2025 15:10:07 +0800 Subject: [PATCH 002/474] Fix jax.where to jnp.where --- src/ripplegw/waveforms/IMRPhenomPv2_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py index fc680b93..095a2701 100644 --- a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py @@ -95,7 +95,7 @@ def convert_spins( thetaJ_sf = jnp.arccos(J0z_sf / J0) no_inplane_J0 = jnp.abs(J0x_sf) < MAX_TOL_ATAN and jnp.abs(J0y_sf) < MAX_TOL_ATAN - phiJ_sf = jax.where( + phiJ_sf = jnp.where( no_inplane_J0, jnp.pi / 2.0 - phiRef, # This is the aligned-spin case jnp.arctan2(J0y_sf, J0x_sf), From 9c2f2213a8bf8a09bbe0be8c90807e7143e2cba5 Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Wed, 16 Apr 2025 03:05:09 +0800 Subject: [PATCH 003/474] Switch to use `&` instead of `and` --- src/ripplegw/waveforms/IMRPhenomPv2_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py index 095a2701..5aa51b28 100644 --- a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py @@ -94,7 +94,7 @@ def convert_spins( thetaJ_sf = jnp.arccos(J0z_sf / J0) - no_inplane_J0 = jnp.abs(J0x_sf) < MAX_TOL_ATAN and jnp.abs(J0y_sf) < MAX_TOL_ATAN + no_inplane_J0 = (jnp.abs(J0x_sf) < MAX_TOL_ATAN) & (jnp.abs(J0y_sf) < MAX_TOL_ATAN) phiJ_sf = jnp.where( no_inplane_J0, jnp.pi / 2.0 - phiRef, # This is the aligned-spin case @@ -124,7 +124,7 @@ def convert_spins( tmp_x, tmp_y, tmp_z = ROTATEY(-thetaJ_sf, tmp_x, tmp_y, tmp_z) tmp_x, tmp_y, tmp_z = ROTATEZ(kappa, tmp_x, tmp_y, tmp_z) - no_inplane_LN = jnp.abs(tmp_x) < MAX_TOL_ATAN and jnp.abs(tmp_y) < MAX_TOL_ATAN + no_inplane_LN = (jnp.abs(tmp_x) < MAX_TOL_ATAN) & (jnp.abs(tmp_y) < MAX_TOL_ATAN) alpha0 = jnp.where( no_inplane_LN, jnp.pi, # This is the aligned-spin case From ac10d8a4a689ea02229144765191e5effd860b99 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Thu, 18 Sep 2025 18:19:18 +0200 Subject: [PATCH 004/474] Added spin weighted spherical harmonics. Cross checked with lal.SpinWeightedSphericalHarmonic --- src/ripplegw/waveforms/spherical_harmonics.py | 76 +++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 src/ripplegw/waveforms/spherical_harmonics.py diff --git a/src/ripplegw/waveforms/spherical_harmonics.py b/src/ripplegw/waveforms/spherical_harmonics.py new file mode 100644 index 00000000..83d29cd6 --- /dev/null +++ b/src/ripplegw/waveforms/spherical_harmonics.py @@ -0,0 +1,76 @@ +import jax +import jax.numpy as jnp +import math + + +def compute_sminus2_l2(theta, m): + """ + Spin -2 weighted spherical harmonic for l=2, phi=0. + theta: float or array + m: integer in [-2, -1, 0, 1, 2] + """ + + # Compute the fac factor based on m + fac = jnp.where(m == -2, jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 - jnp.cos(theta))**2, + jnp.where(m == -1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 - jnp.cos(theta)), + jnp.where(m == 0, jnp.sqrt(15.0 / (32.0 * jnp.pi)) * jnp.sin(theta)**2, + jnp.where(m == 1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 + jnp.cos(theta)), + jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 + jnp.cos(theta))**2 + ) + ) + ) + ) + + return fac + + +def compute_sminus2_l3(theta, m): + """ + Spin -2 weighted spherical harmonic for l=3, phi=0. + theta: scalar or array + m: integer in [-3, 3] + """ + + fac = jnp.where(m == -3, jnp.sqrt(21.0 / (2.0 * jnp.pi)) * jnp.cos(theta / 2.0) * (jnp.sin(theta / 2.0) ** 5), + jnp.where(m == -2, jnp.sqrt(7.0 / (4.0 * jnp.pi)) * (2.0 + 3.0 * jnp.cos(theta)) * (jnp.sin(theta / 2.0) ** 4), + jnp.where(m == -1, jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) + 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, + jnp.where(m == 0, (jnp.sqrt(105.0 / (2.0 * jnp.pi)) * jnp.cos(theta) * (jnp.sin(theta) ** 2)) / 4.0, + jnp.where(m == 1, -jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) - 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, + jnp.where(m == 2, jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**4) * (-2.0 + 3.0 * jnp.cos(theta)) / 2.0, + -jnp.sqrt(21.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * jnp.sin(theta/2.0) + ) + ) + ) + ) + ) + ) + + return fac + + +def compute_sminus2_l4(theta, m): + """ + Spin -2 weighted spherical harmonic for l=4, phi=0. + theta: scalar or array + m: integer in [-4, 4] + """ + + fac = jnp.where(m == -4, 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**2) * (jnp.sin(theta/2.0)**6), + jnp.where(m == -3, 3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * jnp.cos(theta/2.0) * (1.0 + 2.0 * jnp.cos(theta)) * (jnp.sin(theta/2.0)**5), + jnp.where(m == -2, 3.0 * (9.0 + 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta/2.0)**4) / (4.0 * jnp.sqrt(jnp.pi)), + jnp.where(m == -1, 3.0 * (3.0 * jnp.sin(theta) + 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) - 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), + jnp.where(m == 0, 3.0 * jnp.sqrt(5.0 / (2.0 * jnp.pi)) * (5.0 + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta)**2) / 16.0, + jnp.where(m == 1, 3.0 * (3.0 * jnp.sin(theta) - 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) + 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), + jnp.where(m == 2, 3.0 * (jnp.cos(theta/2.0)**4) * (9.0 - 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) / (4.0 * jnp.sqrt(jnp.pi)), + jnp.where(m == 3, -3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * (-1.0 + 2.0 * jnp.cos(theta)) * jnp.sin(theta/2.0), + 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**6) * (jnp.sin(theta/2.0)**2) + ) + ) + ) + ) + ) + ) + ) + ) + + return fac \ No newline at end of file From 8b457d1d5715469f4ea23afb63e8260028e7c928 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Thu, 18 Sep 2025 18:27:24 +0200 Subject: [PATCH 005/474] Adjust syntex --- src/ripplegw/waveforms/spherical_harmonics.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/ripplegw/waveforms/spherical_harmonics.py b/src/ripplegw/waveforms/spherical_harmonics.py index 83d29cd6..59f03264 100644 --- a/src/ripplegw/waveforms/spherical_harmonics.py +++ b/src/ripplegw/waveforms/spherical_harmonics.py @@ -11,7 +11,7 @@ def compute_sminus2_l2(theta, m): """ # Compute the fac factor based on m - fac = jnp.where(m == -2, jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 - jnp.cos(theta))**2, + harmonics = jnp.where(m == -2, jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 - jnp.cos(theta))**2, jnp.where(m == -1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 - jnp.cos(theta)), jnp.where(m == 0, jnp.sqrt(15.0 / (32.0 * jnp.pi)) * jnp.sin(theta)**2, jnp.where(m == 1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 + jnp.cos(theta)), @@ -21,7 +21,7 @@ def compute_sminus2_l2(theta, m): ) ) - return fac + return harmonics def compute_sminus2_l3(theta, m): @@ -31,7 +31,7 @@ def compute_sminus2_l3(theta, m): m: integer in [-3, 3] """ - fac = jnp.where(m == -3, jnp.sqrt(21.0 / (2.0 * jnp.pi)) * jnp.cos(theta / 2.0) * (jnp.sin(theta / 2.0) ** 5), + harmonics = jnp.where(m == -3, jnp.sqrt(21.0 / (2.0 * jnp.pi)) * jnp.cos(theta / 2.0) * (jnp.sin(theta / 2.0) ** 5), jnp.where(m == -2, jnp.sqrt(7.0 / (4.0 * jnp.pi)) * (2.0 + 3.0 * jnp.cos(theta)) * (jnp.sin(theta / 2.0) ** 4), jnp.where(m == -1, jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) + 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, jnp.where(m == 0, (jnp.sqrt(105.0 / (2.0 * jnp.pi)) * jnp.cos(theta) * (jnp.sin(theta) ** 2)) / 4.0, @@ -45,7 +45,7 @@ def compute_sminus2_l3(theta, m): ) ) - return fac + return harmonics def compute_sminus2_l4(theta, m): @@ -55,7 +55,7 @@ def compute_sminus2_l4(theta, m): m: integer in [-4, 4] """ - fac = jnp.where(m == -4, 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**2) * (jnp.sin(theta/2.0)**6), + harmonics = jnp.where(m == -4, 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**2) * (jnp.sin(theta/2.0)**6), jnp.where(m == -3, 3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * jnp.cos(theta/2.0) * (1.0 + 2.0 * jnp.cos(theta)) * (jnp.sin(theta/2.0)**5), jnp.where(m == -2, 3.0 * (9.0 + 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta/2.0)**4) / (4.0 * jnp.sqrt(jnp.pi)), jnp.where(m == -1, 3.0 * (3.0 * jnp.sin(theta) + 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) - 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), @@ -73,4 +73,4 @@ def compute_sminus2_l4(theta, m): ) ) - return fac \ No newline at end of file + return harmonics \ No newline at end of file From a1f62002a0791b1e16b14cf9f33237945e9ff7b1 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Thu, 18 Sep 2025 18:33:26 +0200 Subject: [PATCH 006/474] Adjust spaces --- src/ripplegw/waveforms/spherical_harmonics.py | 57 +++++++------------ 1 file changed, 21 insertions(+), 36 deletions(-) diff --git a/src/ripplegw/waveforms/spherical_harmonics.py b/src/ripplegw/waveforms/spherical_harmonics.py index 59f03264..e73da27b 100644 --- a/src/ripplegw/waveforms/spherical_harmonics.py +++ b/src/ripplegw/waveforms/spherical_harmonics.py @@ -12,14 +12,11 @@ def compute_sminus2_l2(theta, m): # Compute the fac factor based on m harmonics = jnp.where(m == -2, jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 - jnp.cos(theta))**2, - jnp.where(m == -1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 - jnp.cos(theta)), - jnp.where(m == 0, jnp.sqrt(15.0 / (32.0 * jnp.pi)) * jnp.sin(theta)**2, - jnp.where(m == 1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 + jnp.cos(theta)), - jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 + jnp.cos(theta))**2 - ) - ) - ) - ) + jnp.where(m == -1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 - jnp.cos(theta)), + jnp.where(m == 0, jnp.sqrt(15.0 / (32.0 * jnp.pi)) * jnp.sin(theta)**2, + jnp.where(m == 1, jnp.sqrt(5.0 / (16.0 * jnp.pi)) * jnp.sin(theta) * (1.0 + jnp.cos(theta)), + jnp.sqrt(5.0 / (64.0 * jnp.pi)) * (1.0 + jnp.cos(theta))**2 + )))) return harmonics @@ -32,18 +29,13 @@ def compute_sminus2_l3(theta, m): """ harmonics = jnp.where(m == -3, jnp.sqrt(21.0 / (2.0 * jnp.pi)) * jnp.cos(theta / 2.0) * (jnp.sin(theta / 2.0) ** 5), - jnp.where(m == -2, jnp.sqrt(7.0 / (4.0 * jnp.pi)) * (2.0 + 3.0 * jnp.cos(theta)) * (jnp.sin(theta / 2.0) ** 4), - jnp.where(m == -1, jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) + 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, - jnp.where(m == 0, (jnp.sqrt(105.0 / (2.0 * jnp.pi)) * jnp.cos(theta) * (jnp.sin(theta) ** 2)) / 4.0, - jnp.where(m == 1, -jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) - 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, - jnp.where(m == 2, jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**4) * (-2.0 + 3.0 * jnp.cos(theta)) / 2.0, - -jnp.sqrt(21.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * jnp.sin(theta/2.0) - ) - ) - ) - ) - ) - ) + jnp.where(m == -2, jnp.sqrt(7.0 / (4.0 * jnp.pi)) * (2.0 + 3.0 * jnp.cos(theta)) * (jnp.sin(theta / 2.0) ** 4), + jnp.where(m == -1, jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) + 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, + jnp.where(m == 0, (jnp.sqrt(105.0 / (2.0 * jnp.pi)) * jnp.cos(theta) * (jnp.sin(theta) ** 2)) / 4.0, + jnp.where(m == 1, -jnp.sqrt(35.0 / (2.0 * jnp.pi)) * (jnp.sin(theta) - 4.0 * jnp.sin(2.0*theta) - 3.0 * jnp.sin(3.0*theta)) / 32.0, + jnp.where(m == 2, jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**4) * (-2.0 + 3.0 * jnp.cos(theta)) / 2.0, + -jnp.sqrt(21.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * jnp.sin(theta/2.0) + )))))) return harmonics @@ -56,21 +48,14 @@ def compute_sminus2_l4(theta, m): """ harmonics = jnp.where(m == -4, 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**2) * (jnp.sin(theta/2.0)**6), - jnp.where(m == -3, 3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * jnp.cos(theta/2.0) * (1.0 + 2.0 * jnp.cos(theta)) * (jnp.sin(theta/2.0)**5), - jnp.where(m == -2, 3.0 * (9.0 + 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta/2.0)**4) / (4.0 * jnp.sqrt(jnp.pi)), - jnp.where(m == -1, 3.0 * (3.0 * jnp.sin(theta) + 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) - 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), - jnp.where(m == 0, 3.0 * jnp.sqrt(5.0 / (2.0 * jnp.pi)) * (5.0 + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta)**2) / 16.0, - jnp.where(m == 1, 3.0 * (3.0 * jnp.sin(theta) - 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) + 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), - jnp.where(m == 2, 3.0 * (jnp.cos(theta/2.0)**4) * (9.0 - 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) / (4.0 * jnp.sqrt(jnp.pi)), - jnp.where(m == 3, -3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * (-1.0 + 2.0 * jnp.cos(theta)) * jnp.sin(theta/2.0), - 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**6) * (jnp.sin(theta/2.0)**2) - ) - ) - ) - ) - ) - ) - ) - ) + jnp.where(m == -3, 3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * jnp.cos(theta/2.0) * (1.0 + 2.0 * jnp.cos(theta)) * (jnp.sin(theta/2.0)**5), + jnp.where(m == -2, 3.0 * (9.0 + 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta/2.0)**4) / (4.0 * jnp.sqrt(jnp.pi)), + jnp.where(m == -1, 3.0 * (3.0 * jnp.sin(theta) + 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) - 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), + jnp.where(m == 0, 3.0 * jnp.sqrt(5.0 / (2.0 * jnp.pi)) * (5.0 + 7.0 * jnp.cos(2.0*theta)) * (jnp.sin(theta)**2) / 16.0, + jnp.where(m == 1, 3.0 * (3.0 * jnp.sin(theta) - 2.0 * jnp.sin(2.0*theta) + 7.0 * jnp.sin(3.0*theta) + 7.0 * jnp.sin(4.0*theta)) / (32.0 * jnp.sqrt(2.0 * jnp.pi)), + jnp.where(m == 2, 3.0 * (jnp.cos(theta/2.0)**4) * (9.0 - 14.0 * jnp.cos(theta) + 7.0 * jnp.cos(2.0*theta)) / (4.0 * jnp.sqrt(jnp.pi)), + jnp.where(m == 3, -3.0 * jnp.sqrt(7.0 / (2.0 * jnp.pi)) * (jnp.cos(theta/2.0)**5) * (-1.0 + 2.0 * jnp.cos(theta)) * jnp.sin(theta/2.0), + 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**6) * (jnp.sin(theta/2.0)**2) + )))))))) return harmonics \ No newline at end of file From 88c5b235d71ad1f67625b1848b7544c79ca864e3 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Thu, 18 Sep 2025 19:08:19 +0200 Subject: [PATCH 007/474] Port computation of spin weighted spherical harmonics --- src/ripplegw/waveforms/IMRPhenomXPHM.py | 156 ++++++++++++++++++++++++ 1 file changed, 156 insertions(+) create mode 100644 src/ripplegw/waveforms/IMRPhenomXPHM.py diff --git a/src/ripplegw/waveforms/IMRPhenomXPHM.py b/src/ripplegw/waveforms/IMRPhenomXPHM.py new file mode 100644 index 00000000..8a0d9b32 --- /dev/null +++ b/src/ripplegw/waveforms/IMRPhenomXPHM.py @@ -0,0 +1,156 @@ + +import jax +import jax.numpy as jnp +from .IMRPhenomD_utils import ( + get_coeffs, + get_delta0, + get_delta1, + get_delta2, + get_delta3, + get_delta4, + get_transition_frequencies, +) + +from .IMRPhenomD_QNMdata import fM_CUT +from ..constants import EulerGamma, gt, m_per_Mpc, C, PI +from ..typing import Array +from ripplegw import Mc_eta_to_ms +from .spherical_harmonics import (compute_sminus2_l2, compute_sminus2_l3, compute_sminus2_l4) + + +def test_link(): + print('link OK') + return + + +''' +Steps to construct the XPHM waveform from relative binning project + +### L-frame strain of each mode + lalsim.SimIMRPhenomXPHMFrequencySequenceOneMode + +### C prefactors + lalsim.SimIMRPhenomXPMSAAngles + + + +### Order of C prefactor computations: + 1. Euler angles + twist_factor = C prefactors + + 2. Euler angles ingredients: + A. lalsim.SimIMRPhenomXPMSAAngles + + 3. Twist factor ingredients + A. Transfer functions + a. Wigner coefficients + B. Wigner coefficients + C. y2lm + + +### multiply twisting up factors with the L-frame mode and sum all modes + +''' + + + + +def compute_zeta(params: Array): + + zeta = None + return zeta + + + + +def compute_m2ylm(l: int, m: int, theta_jn: float): + """ + Computes the -2 spin weighted spherical harmonic evaluated at theta = theta_jn, phi = 0. + l: l index + m: m index + theta_jn: theta_jn angle + """ + + m2ylm = jnp.where(l==2, compute_sminus2_l2(theta_jn, m), + jnp.where(l==3, compute_sminus2_l3(theta_jn, m), + jnp.where(l==4, compute_sminus2_l4(theta_jn, m), jnp.nan) + ) + ) + + return m2ylm + +def compute_transfer_function(l: float, m: float, mprime: float, alpha: float, beta: float, theta_jn: float): + + pos_wigner_coefficient = compute_wigner_coefficient(l, m, mprime, beta) + neg_wigner_coefficient = compute_wigner_coefficient(l, -m, mprime, beta) + negative_power = (-1)**(l+m) + + + + term_a = jnp.exp(-1j*m*alpha) * pos_wigner_coefficient[0] * compute_m2ylm(l, m, theta_jn) + + term_b = negative_power * jnp.exp(-1j*m*alpha) * pos_wigner_coefficient[1] * compute_m2ylm(l, m, theta_jn) + + term_c = jnp.exp(1j*m*alpha) * neg_wigner_coefficient[0] * compute_m2ylm(l, -m, theta_jn) + + term_d = negative_power * jnp.exp(1j*m*alpha) * neg_wigner_coefficient[1] * compute_m2ylm(l, -m, theta_jn) + + return term_a, term_b, term_c, term_d + + +def compute_wigner_coefficient(): + return None + + +def compute_twist_factor_plus_cross(l: float, mprime: float, theta_jn: float, alpha: Array, beta: Array, gamma: Array): + + m_array = jnp.arange(1, l+1, 1) + plus_summand = 0 + cross_summand = 0 + + for m in m_array: + transfer = compute_transfer_function(l, m, mprime, alpha, beta, theta_jn) + term_1 = transfer[1] + transfer[3] + term_2 = ((-1)**l)*jnp.conj(transfer[0] + transfer[2]) + plus_summand += term_1 + term_2 + cross_summand += term_1 - term_2 + + + wigner_coefficient = compute_wigner_coefficient(l, 0, mprime, beta) + + term_1 = ((-1)**l) * wigner_coefficient[1] * compute_m2ylm(l, 0, theta_jn) + term_2 = ((-1)**l) * wigner_coefficient[0] * compute_m2ylm(l, 0, theta_jn) + + + plus_summand += term_1 + term_2 + cross_summand += term_1 - term_2 + + return 0.5*jnp.exp(1*mprime*gamma)*plus_summand, 1j*0.5*jnp.exp(1*mprime*gamma)*cross_summand + + +def compute_c_prefactors(f: Array, params: Array, X: float): + + c_plus_j, c_cross_j = compute_twist_factor_plus_cross() + + zeta = compute_zeta(params) + + c_plus = jnp.cos(2*zeta)*c_plus_j + jnp.sin(2*zeta)*c_cross_j + + c_cross = jnp.cos(2*zeta)*c_cross_j - jnp.sin(2*zeta)*c_plus_j + + + return c_plus, c_cross + + + + +def generate_hlframe_waveform(f: Array, params: Array, f_ref: float, l: float, m: float): + + hlframe = None + return hlframe + + +def gen_IMRPhenomXPHM_hphc(f: Array, params: Array, f_ref: float): + hp = 0 + hc = 0 + return hp, hc + From 3a7a3728348ca07c4a2f95df39dd9180c9759025 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Thu, 18 Sep 2025 19:24:22 +0200 Subject: [PATCH 008/474] checkpoint for XPHM --- src/ripplegw/waveforms/IMRPhenomXPHM.py | 37 +++++++++++++++---------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXPHM.py b/src/ripplegw/waveforms/IMRPhenomXPHM.py index 8a0d9b32..6d75ab19 100644 --- a/src/ripplegw/waveforms/IMRPhenomXPHM.py +++ b/src/ripplegw/waveforms/IMRPhenomXPHM.py @@ -1,6 +1,8 @@ import jax import jax.numpy as jnp +from jax import vmap + from .IMRPhenomD_utils import ( get_coeffs, get_delta0, @@ -41,10 +43,10 @@ def test_link(): A. lalsim.SimIMRPhenomXPMSAAngles 3. Twist factor ingredients - A. Transfer functions + A. Transfer functions (Done) a. Wigner coefficients B. Wigner coefficients - C. y2lm + C. y2lm (done) ### multiply twisting up factors with the L-frame mode and sum all modes @@ -78,7 +80,9 @@ def compute_m2ylm(l: int, m: int, theta_jn: float): return m2ylm -def compute_transfer_function(l: float, m: float, mprime: float, alpha: float, beta: float, theta_jn: float): +def compute_transfer_function(l: int, m: int, mprime: int, alpha: Array, beta: Array, theta_jn: float): + + ### substitute Atransfer_slow pos_wigner_coefficient = compute_wigner_coefficient(l, m, mprime, beta) neg_wigner_coefficient = compute_wigner_coefficient(l, -m, mprime, beta) @@ -102,27 +106,30 @@ def compute_wigner_coefficient(): def compute_twist_factor_plus_cross(l: float, mprime: float, theta_jn: float, alpha: Array, beta: Array, gamma: Array): + ### substitute: twist_factor_slow_plus_cross - m_array = jnp.arange(1, l+1, 1) - plus_summand = 0 - cross_summand = 0 - - for m in m_array: + def body(m): transfer = compute_transfer_function(l, m, mprime, alpha, beta, theta_jn) term_1 = transfer[1] + transfer[3] - term_2 = ((-1)**l)*jnp.conj(transfer[0] + transfer[2]) - plus_summand += term_1 + term_2 - cross_summand += term_1 - term_2 + term_2 = ((-1) ** l) * jnp.conj(transfer[0] + transfer[2]) + plus_contrib = term_1 + term_2 + cross_contrib = term_1 - term_2 + return plus_contrib, cross_contrib + + # Vectorize over m + plus_vals, cross_vals = vmap(body)(jnp.arange(1, l + 1, 1)) + plus_summand = jnp.sum(plus_vals) + cross_summand = jnp.sum(cross_vals) wigner_coefficient = compute_wigner_coefficient(l, 0, mprime, beta) - term_1 = ((-1)**l) * wigner_coefficient[1] * compute_m2ylm(l, 0, theta_jn) - term_2 = ((-1)**l) * wigner_coefficient[0] * compute_m2ylm(l, 0, theta_jn) + term_alpha = ((-1)**l) * wigner_coefficient[1] * compute_m2ylm(l, 0, theta_jn) + term_beta = ((-1)**l) * wigner_coefficient[0] * compute_m2ylm(l, 0, theta_jn) - plus_summand += term_1 + term_2 - cross_summand += term_1 - term_2 + plus_summand += term_alpha + term_beta + cross_summand += term_alpha - term_beta return 0.5*jnp.exp(1*mprime*gamma)*plus_summand, 1j*0.5*jnp.exp(1*mprime*gamma)*cross_summand From be93a705daf64b11dabec225d5a26dd04da3cbda Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Mon, 22 Sep 2025 17:46:29 +0200 Subject: [PATCH 009/474] Sphl harmonics and msa angles --- src/ripplegw/waveforms/msa.py | 359 ++++++++++++++++++ src/ripplegw/waveforms/spherical_harmonics.py | 6 +- 2 files changed, 364 insertions(+), 1 deletion(-) create mode 100644 src/ripplegw/waveforms/msa.py diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py new file mode 100644 index 00000000..41558ba9 --- /dev/null +++ b/src/ripplegw/waveforms/msa.py @@ -0,0 +1,359 @@ +import jax +import jax.numpy as jnp +import math +from ..typing import Array + + + +def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime: int): + ## XLALSimIMRPhenomXPMSAAngles + + ''' + lalsim.SimIMRPhenomXPMSAAngles(f_seq, + params['mass_1'] * lal.MSUN_SI, + params['mass_2'] * lal.MSUN_SI, + params['chi_1x'], params['chi_1y'], params['chi_1z'], + params['chi_2x'], params['chi_2y'], params['chi_2z'], + 0, ### this is inclination which is set to zero. It needs to be set to params['inclination'] if you want to use PhenomPNR waveforms + waveform_arugments['reference_frequency'], mprime, + lalDict_MSA); + + ''' + + default_prec_version = 221 + piGM = 1 + pWF = None + pPrec = None + v = freq * piGM * (2/mprime) ### v is the frequency. Not velocity. To be double checked + v = v**(1/3) + vangles = jax.vmap(lambda v_: IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v_, pWF, pPrec))(v) + + + + alpha_of_f = vangles.x - pPrec.alpha_offset + gamma_of_f = -1 * (vangles.y - pPrec.epsilon_offset) + cosbeta_of_f = vangles.z + + return alpha_of_f, gamma_of_f, cosbeta_of_f + + +def IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v: Array, pWF, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a8a1f3cefac1e80942b2848ca0133bcbc + + v: Velocity + pWF: IMRPhenomX waveform struct + pPrec: IMRPhenomX precession struct + """ + + vout = {0.,0.,0.} + + L_norm = pWF.eta / v + J_norm = IMRPhenomX_JNorm_MSA(L_norm, pPrec) + + L_norm3PN = 0.0 + + if pPrec.IMRPhenomXPrecVersion == 'blah': + L_norm3PN = IMRPhenomX_L_norm_3PN_of_v(v, v*v, L_norm, pPrec) + else: + print('Do somethig else') + + + J_norm3PN = IMRPhenomX_JNorm_MSA(L_norm3PN,pPrec) + + + vRoots = IMRPhenomX_Return_Roots_MSA(L_norm,J_norm,pPrec) + + + pPrec.S32 = vRoots.x + pPrec.Smi2 = vRoots.y + pPrec.Spl2 = vRoots.z + + ### define following four from the three above + pPrec.Spl2mSmi2 + pPrec.Spl2pSmi2 + pPrec.Spl + pPrec.Smi + + ### New function + SNorm = IMRPhenomX_Return_SNorm_MSA(v,pPrec) + pPrec.S_norm = SNorm + pPrec.S_norm_2 = SNorm * SNorm + + vMSA = {0.,0.,0.} + if jnp.abs(pPrec.Smi2 - pPrec.Spl2) > 1.e-5: + vMSA = IMRPhenomX_Return_MSA_Corrections_MSA(v, L_norm, J_norm, pPrec) + + + + phiz_MSA = vMSA.x + zeta_MSA = vMSA.y + + phiz = IMRPhenomX_Return_phiz_MSA(v,J_norm,pPrec) + zeta = IMRPhenomX_Return_zeta_MSA(v,pPrec) + cos_theta_L = IMRPhenomX_costhetaLJ(L_norm3PN,J_norm3PN,SNorm) + + vout_x = phiz + phiz_MSA + vout_y = zeta + zeta_MSA + vout_z = cos_theta_L + + + return vout_x, vout_y, vout_z + + +def IMRPhenomX_JNorm_MSA(LNorm: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#aca1fc9b93acf010a788cdc02cfb92631 + """ + JNorm2 = (LNorm*LNorm + (2.0 * LNorm * pPrec.c1_over_eta) + pPrec.SAv2) + return JNorm2 + + +def IMRPhenomX_Return_Roots_MSA(LNorm: float, JNorm: float, pPrec): + + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a3d6fc6f6edff39a14e884521a3cd2997 + """ + vout = None + + tmp1 = 0.0 + tmp2 = 0.0 + tmp3 = 0.0 + tmp4 = 0.0 + tmp5 = 0.0 + tmp6 = 0.0 + + vBCD = IMRPhenomX_Return_Spin_Evolution_Coefficients_MSA(LNorm, JNorm, pPrec) + + B = vBCD.x + C = vBCD.y + D = vBCD.z + + S1Norm2 = pPrec.S1_norm_2 + S2Norm2 = pPrec.S2_norm_2 + + S0Norm2 = pPrec.S_0_norm_2 + + B2 = B * B + B3 = B2 * B + BC = B * C + + p = C - B2 / 3 + qc = (2.0/27.0)*B3 - BC/3.0 + D + + sqrtarg = jnp.sqrt(-p/3.0) + acosarg = 1.5 * qc/p/sqrtarg + + acosarg = jnp.clip(acosarg, -1.0, 1.0) + + theta = jnp.arccos(acosarg) / 3.0 + cos_theta = jnp.cos(theta) + + dotS1Ln = pPrec.dotS1Ln + dotS2Ln = pPrec.dotS2Ln + + + ############# If block condition ########################## + + + condition = ((theta != theta) | (sqrtarg != sqrtarg) | (dotS1Ln == 1) | (dotS2Ln == 1) | (dotS1Ln == -1) | (dotS2Ln == -1) | (S1Norm2 == 0) | (S2Norm2 == 0)) + + ############# else block for the condition above ########################## + + tmp1 = 2.0 * sqrtarg * jnp.cos(theta - 2.0 * 2.0 * jnp.pi / 3.0) - B / 3.0 + tmp2 = 2.0 * sqrtarg * jnp.cos(theta - 2*jnp.pi / 3.0) - B / 3.0 + tmp3 = 2.0 * sqrtarg * jnp.cos_theta - B / 3.0 + tmp4 = jnp.maximum(jnp.maximum(tmp1, tmp2), tmp3) + tmp5 = jnp.minimum(jnp.minimum(tmp1, tmp2), tmp3) + cond_tmp6_tmp3 = (tmp4 - tmp3) > 0.0 & (tmp5 - tmp3) < 0.0 + cond_tmp6_tmp1 = (tmp4 - tmp1) > 0.0 & (tmp5 - tmp1) < 0.0 + tmp6 = jnp.where(cond_tmp6_tmp3, tmp3, jnp.where(cond_tmp6_tmp1, tmp1, tmp2)) + + tmp4 = jnp.abs(tmp4) + tmp6 = jnp.abs(tmp6) + + ############# All possibilities evalusted. Now assign the values ########################## + + S32 = jnp.where(condition, 0.0, tmp5) + Smi2 = jnp.where(condition, S0Norm2, tmp6) + Spl2 = jnp.where(condition, Smi2 + 1e-9, tmp4) + + return S32, Smi2, Spl2 + + + +def IMRPhenomX_Return_Spin_Evolution_Coefficients_MSA(LNorm: float, JNorm: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a1d96ad5bf7cd20cc0426c0fb6c5135ca + """ + JNorm2 = JNorm * JNorm + LNorm2 = LNorm * LNorm + S1Norm2 = pPrec.S1_norm_2 + S2Norm2 = pPrec.S2_norm_2 + + + q = pPrec.qq + eta = pPrec.eta + + J2mL2 = (JNorm2 - LNorm2) + J2mL2Sq = J2mL2 * J2mL2 + + delta = pPrec.delta_qq + deltaSq = delta*delta + + Seff = pPrec.Seff + + vout_x = (LNorm2 + S1Norm2)*q + 2.0*LNorm*Seff - 2.0*JNorm2 - S1Norm2 - S2Norm2 + (LNorm2 + S2Norm2)/q + vout_y = J2mL2Sq - 2.0*LNorm*Seff*J2mL2 - 2.0*((1.0 - q)/q)*LNorm2*(S1Norm2 - q*S2Norm2) + 4.0*eta*LNorm2*Seff*Seff - 2.0*delta*(S1Norm2 - S2Norm2)*Seff*LNorm + 2.0*((1.0 - q)/q)*(q*S1Norm2 - S2Norm2)*JNorm2 + vout_z = ((1.0 - q)/q)*(S2Norm2 - q*S1Norm2)*J2mL2Sq + deltaSq*(S1Norm2 - S2Norm2)*(S1Norm2 - S2Norm2)*LNorm2/eta + 2.0*delta*LNorm*Seff*(S1Norm2 - S2Norm2)*J2mL2 + + return vout_x, vout_y, vout_z + + +def IMRPhenomX_L_norm_3PN_of_v(v: float, v2: float, L_norm: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#aa4bb5fdf7feb7f787c02ce5d4e6b4b88 + """ + return L_norm * (1. + v2*(pPrec.constants_L[0] + v*pPrec.constants_L[1] + v2*(pPrec.constants_L[2] + v*pPrec.constants_L[3] + v2*(pPrec.constants_L[4])))) + + +def IMRPhenomX_Return_SNorm_MSA(v: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a5620d4146607de6696bf42bd346adcbf + + remaining functions: gsl_sf_elljac_e + """ + + v2 = v*v + + #If spin norms ~ cancel then we do not need to evaluate the Jacobi elliptic function. Check tolerance? + + if jnp.abs(pPrec.Smi2 - pPrec.Spl2) < 1.0e-5: + + sn = 0.0 + + else: + + m = (pPrec.Smi2 - pPrec.Spl2) / (pPrec.S32 - pPrec.Spl2) + + psi = IMRPhenomX_psiofv(v, v2, pPrec.psi0, pPrec.psi1, pPrec.psi2, pPrec) + + # Evaluate the Jacobi ellptic functions + sn, cn, dn = gsl_sf_elljac_e(psi, m) + + + SNorm2 = pPrec.Spl2 + (pPrec.Smi2 - pPrec.Spl2)*sn*sn + + return jnp.sqrt(SNorm2) + +def IMRPhenomX_psiofv(v: float, v2: float, psi0: float, psi1: float, psi2: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a03b4dac8d2e93b6ab44bc1539981f5ce + """ + return ( psi0 - 0.75*pPrec.g0 * pPrec.delta_qq * (1.0 + psi1*v + psi2*v2) / (v2*v) ) + +def IMRPhenomX_Return_MSA_Corrections_MSA(): + return None + + +def IMRPhenomX_costhetaLJ(L_norm: float, J_norm: float, S_norm: float): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#aa7c77bf203a2f25482647de7ab325548 + """ + + costhetaLJ = 0.5 * (J_norm**2 + L_norm**2 - S_norm**2) / (L_norm * J_norm) + costhetaLJ = jnp.clip(costhetaLJ, -1.0, 1.0) + + return costhetaLJ + + + +def IMRPhenomX_Return_zeta_MSA(v: Array, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a2f8434d98da248e9b6d4095bf13c0aa2 + """ + + invv = 1.0/v + invv2 = invv**2 + invv3 = invv2 * invv + v2 = v*v + logv = jnp.log(v) ### FIXME log or ln. Check base in lalsuite + + zeta_out = pPrec.eta * ( + pPrec.Omegazeta0_coeff*invv3 + + pPrec.Omegazeta1_coeff*invv2 + + pPrec.Omegazeta2_coeff*invv + + pPrec.Omegazeta3_coeff*logv + + pPrec.Omegazeta4_coeff*v + + pPrec.Omegazeta5_coeff*v2) + + pPrec.zeta_0 + + + zeta_out = jnp.where(jnp.isnan(zeta_out), 0.0, zeta_out) #### If zeta_out is nan, the function will return 0. + + + return zeta_out + + + +def IMRPhenomX_Return_phiz_MSA(v: Array, JNorm, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#acc03f055e60854037e30ade700d56b5d + """ + + invv = 1.0 / v + invv2 = invv * invv + + LNewt = pPrec.eta / v + + c1 = pPrec.c1 + c12 = c1 * c1 + + SAv2 = pPrec.SAv2 + SAv = pPrec.SAv + invSAv = pPrec.invSAv + invSAv2 = pPrec.invSAv2 + + + log1 = jnp.log(jnp.abs(c1 + JNorm * pPrec.eta + pPrec.eta * LNewt)) + log2 = jnp.log(jnp.abs(c1 + JNorm * SAv * v + SAv2 * v)) + + + phiz_0_coeff = (JNorm * pPrec.inveta4) * (0.5*c12 - c1*pPrec.eta2*invv/6.0 - SAv2*pPrec.eta2/3.0 - pPrec.eta4*invv2/3.0) - ((c1 * 0.5 * pPrec.inveta) * (c12 * pPrec.inveta4 - SAv2 * pPrec.inveta2) * log1) + phiz_1_coeff = - 0.5 * JNorm * pPrec.inveta2 * (c1 + pPrec.eta * LNewt) + 0.5*pPrec.inveta3 * (c12 - pPrec.eta2*SAv2) * log1 + phiz_2_coeff = -JNorm + SAv*log2 - c1*log1*pPrec.inveta + phiz_3_coeff = JNorm*v - pPrec.eta*log1 + c1*log2*pPrec.invSAv + phiz_4_coeff = (0.5*JNorm*invSAv2*v)*(c1 + v*SAv2) - (0.5*invSAv2*invSAv)*(c12 - pPrec.eta2*SAv2)*log2 + phiz_5_coeff = -JNorm*v*( (0.5*c12*invSAv2*invSAv2) - (c1*v*invSAv2/6.0) - v*v/3.0 - pPrec.eta2*invSAv2/3.0) + (0.5*c1*invSAv2*invSAv2*invSAv)*(c12 - pPrec.eta2*SAv2)*log2 + + + phiz_out = (phiz_0_coeff * pPrec.Omegaz0_coeff + + phiz_1_coeff * pPrec.Omegaz1_coeff + + phiz_2_coeff * pPrec.Omegaz2_coeff + + phiz_3_coeff * pPrec.Omegaz3_coeff + + phiz_4_coeff * pPrec.Omegaz4_coeff + + phiz_5_coeff * pPrec.Omegaz5_coeff + + pPrec.phiz_0) + + + phiz_out = jnp.where(jnp.isnan(phiz_out), 0.0, phiz_out) #### If zeta_out is nan, the function will return 0. + + return (phiz_out) + + + + +class IMRPhenomXPrecessionStruct: + def __init__(self, eta, Omegazeta0_coeff, Omegazeta1_coeff, Omegazeta2_coeff, + Omegazeta3_coeff, Omegazeta4_coeff, Omegazeta5_coeff, zeta_0): + self.eta = eta + self.Omegazeta0_coeff = Omegazeta0_coeff + self.Omegazeta1_coeff = Omegazeta1_coeff + self.Omegazeta2_coeff = Omegazeta2_coeff + self.Omegazeta3_coeff = Omegazeta3_coeff + self.Omegazeta4_coeff = Omegazeta4_coeff + self.Omegazeta5_coeff = Omegazeta5_coeff + self.zeta_0 = zeta_0 + + + diff --git a/src/ripplegw/waveforms/spherical_harmonics.py b/src/ripplegw/waveforms/spherical_harmonics.py index e73da27b..60aefd70 100644 --- a/src/ripplegw/waveforms/spherical_harmonics.py +++ b/src/ripplegw/waveforms/spherical_harmonics.py @@ -1,6 +1,7 @@ import jax import jax.numpy as jnp import math +from ..typing import Array def compute_sminus2_l2(theta, m): @@ -58,4 +59,7 @@ def compute_sminus2_l4(theta, m): 3.0 * jnp.sqrt(7.0 / jnp.pi) * (jnp.cos(theta/2.0)**6) * (jnp.sin(theta/2.0)**2) )))))))) - return harmonics \ No newline at end of file + return harmonics + + + From a4e4756a6b9e8a8abb8440e5abdbc0d6288aea6c Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Mon, 22 Sep 2025 18:05:46 +0200 Subject: [PATCH 010/474] Comment for future dev --- src/ripplegw/waveforms/msa.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py index 41558ba9..edf74693 100644 --- a/src/ripplegw/waveforms/msa.py +++ b/src/ripplegw/waveforms/msa.py @@ -5,6 +5,14 @@ +""" +Remaining function in this module: + +gsl_sf_elljac_e + +This does not exist in numpy or jax.scipy.special. +""" + def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime: int): ## XLALSimIMRPhenomXPMSAAngles @@ -252,7 +260,11 @@ def IMRPhenomX_psiofv(v: float, v2: float, psi0: float, psi1: float, psi2: float """ return ( psi0 - 0.75*pPrec.g0 * pPrec.delta_qq * (1.0 + psi1*v + psi2*v2) / (v2*v) ) -def IMRPhenomX_Return_MSA_Corrections_MSA(): +def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a1aa2393a086fd52c37808975ed9086dc + """ + return None From 73f0662a6c9c655c67d0171a6f55116161a1e6c5 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 23 Sep 2025 14:27:43 +0200 Subject: [PATCH 011/474] Additional utility functions into msa --- src/ripplegw/waveforms/msa.py | 176 +++++++++++++++++++++++++++++++++- 1 file changed, 175 insertions(+), 1 deletion(-) diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py index edf74693..280cffe5 100644 --- a/src/ripplegw/waveforms/msa.py +++ b/src/ripplegw/waveforms/msa.py @@ -24,7 +24,7 @@ def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime params['chi_2x'], params['chi_2y'], params['chi_2z'], 0, ### this is inclination which is set to zero. It needs to be set to params['inclination'] if you want to use PhenomPNR waveforms waveform_arugments['reference_frequency'], mprime, - lalDict_MSA); + lalDict_MSA) ''' @@ -264,9 +264,183 @@ def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, """ lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a1aa2393a086fd52c37808975ed9086dc """ + pflag = pPrec.IMRPhenomXPrecVersion + v2 = v * v + vMSA = jnp.array([0., 0., 0.]) + + c_vec = IMRPhenomX_Return_Constants_c_MSA(v, JNorm, pPrec) + d_vec = IMRPhenomX_Return_Constants_d_MSA(LNorm, JNorm, pPrec) + + + c0 = c_vec.x + c2 = c_vec.y + c4 = c_vec.z + + d0 = d_vec.x + d2 = d_vec.y + d4 = d_vec.z + + #Pre-cache a bunch of useful variables + two_d0 = 2.0 * d0 + + #Eq. B20 of Chatziioannou et al, PRD 95, 104004, (2017), arXiv:1703.03967 + sd = jnp.sqrt( jnp.abs(d2*d2 - 4.0*d0*d4) ) + + #Eq. F20 of Chatziioannou et al, PRD 95, 104004, (2017), arXiv:1703.03967 + A_theta_L = 0.5 * ( (JNorm/LNorm) + (LNorm/JNorm) - (pPrec.Spl2 / (JNorm * LNorm)) ) + + #Eq. F21 of Chatziioannou et al, PRD 95, 104004, (2017), arXiv:1703.03967 + B_theta_L = 0.5 * pPrec.Spl2mSmi2 / (JNorm * LNorm) + + #Coefficients for B16 + nc_num = 2.0*(d0 + d2 + d4) + nc_denom = two_d0 + d2 + sd + + #Equations B16 and B17 respectively + nc = nc_num / nc_denom + nd = nc_denom / two_d0 + + sqrt_nc = jnp.sqrt(jnp.abs(nc)) + sqrt_nd = jnp.sqrt(jnp.abs(nd)) + + #Get phase and phase evolution of S + psi = IMRPhenomX_Return_Psi_MSA(v,v2,pPrec) + pPrec.psi0 + psi_dot = IMRPhenomX_Return_Psi_dot_MSA(v,pPrec) + + #Trigonometric calls are expensive, pre-cache them + #// Note: arctan(tan(x)) = 0 if and only if x \in (−pi/2,pi/2). + tan_psi = jnp.tan(psi) + atan_psi = jnp.arctan(tan_psi) + + #Eq. B18 + C1 = -0.5 * (c0/d0 - 2.0*(c0+c2+c4)/nc_num) + + #Eq. B19 + C2num = c0*( -2.0*d0*d4 + d2*d2 + d2*d4 ) - c2*d0*( d2 + 2.0*d4 ) + c4*d0*( two_d0 + d2 ) + C2den = 2.0 * d0 * sd * (d0 + d2 + d4) + C2 = C2num / C2den + + #These are defined in Appendix B, B14 and B15 respectively + Cphi = (C1 + C2) + Dphi = (C1 - C2) + + ########### if...else statment to determine phiz_0_MSA_Cphi_term ##### + case_nc1 = 0.0 + case_pflag = jnp.abs( (c4 * d0 * ((2*d0+d2) + sd) - c2 * d0 * ((d2+2.*d4) - sd) - c0 * ((2*d0*d4) - (d2+d4) * (d2 - sd))) / (C2den)) * (sqrt_nc / (nc - 1.) * (atan_psi - jnp.atan(sqrt_nc * tan_psi))) / psi_dot + case_default = ( (Cphi / psi_dot) * sqrt_nc / (nc - 1.0) ) * jnp.arctan( ( (1.0 - sqrt_nc) * tan_psi ) / ( 1.0 + (sqrt_nc * tan_psi * tan_psi) ) ) + + + case_else = jnp.where((pflag==222) | (pflag==223), case_pflag, case_default) + phiz_0_MSA_Cphi_term = jnp.where(nc==1.0, case_nc1, case_else) + + + ########### if...else statment to determine phiz_0_MSA_Dphi_term ##### + case_nd1 = 0.0 + case_pflag = jnp.abs( (-c4 * d0 * ((2*d0+d2) - sd) + c2 * d0 * ((d2+2.*d4) + sd) - c0 * (-(2*d0*d4) + (d2+d4) * (d2 + sd)))) / (C2den) * (sqrt_nd / (nd - 1.) * (atan_psi - jnp.arctan(sqrt_nd * tan_psi))) / psi_dot + case_default = ( (Dphi / psi_dot) * sqrt_nd / (nd - 1.0) ) * jnp.arctan( ( (1.0 - sqrt_nd) * tan_psi ) / ( 1.0 + (sqrt_nd * tan_psi * tan_psi) ) ) + + case_else = jnp.where((pflag==222) | (pflag==223), None, None) + phiz_0_MSA_Dphi_term = jnp.where(nd==1, case_nd1, case_else) + + + ################# computing the x part of vMSA ##### + + vMSA_x = ( phiz_0_MSA_Cphi_term + phiz_0_MSA_Dphi_term ) + vMSA.at[0].set(vMSA_x) + + ######## computing the y part of vMSA ##### + ### The first MSA correction to \zeta as given in Eq. F19 + + case_pflag = A_theta_L*vMSA.x + 2.*B_theta_L*d0*(phiz_0_MSA_Cphi_term/(sd-d2) - phiz_0_MSA_Dphi_term/(sd+d2)) + case_default = ( ( A_theta_L * (Cphi + Dphi) ) + (2.0 * d0 * B_theta_L) * ( ( Cphi / (sd - d2) ) - ( Dphi / (sd + d2) ) ) ) / psi_dot + + vMSA_y = jnp.where((pflag==222) | (pflag == 223) | (pflag==224), case_pflag, case_default) + + vMSA.at[1].set(vMSA_y) + + + ################# checking for NaNs #################### + + vMSA = jnp.where(jnp.isnan(vMSA), 0.0, vMSA) + vMSA = vMSA.at[2].set(0.0) + + return vMSA + + + +def IMRPhenomX_Return_Psi_MSA(v: float, v2: flat, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a04a087cab1ff0f38f9dfe4f636170531 + """ + return ( -0.75 * pPrec.g0 * pPrec.delta_qq * (1.0 + pPrec.psi1*v + pPrec.psi2*v2) / (v2*v) ) + +def IMRPhenomX_Return_Psi_dot_MSA(v: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a15cf6139be84a674e0dd9aa8b06ee234 + """ + v2 = v*v + A_coeff = -1.5 * (v2 * v2 * v2) * (1.0 - v*pPrec.Seff) * pPrec.sqrt_inveta + psi_dot = 0.5 * A_coeff * jnp.sqrt(pPrec.Spl2 - pPrec.S32) + return psi_dot + +def IMRPhenomX_Return_Constants_c_MSA(v: float, JNorm: float, pPrec): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#ac70567f34aaea271a8fddc3638214732 + """ + + v2 = v*v + v3 = v*v2 + v4 = v2*v2 + v6 = v3*v3 + + JNorm2 = JNorm * JNorm + + vector vout = {0.,0.,0.} + + Seff = pPrec->Seff + + if(pPrec->IMRPhenomXPrecVersion != 220) + { + // Equation B6 of Chatziioannou et al, PRD 95, 104004, (2017) + vout.x = JNorm * ( 0.75*(1.0 - Seff*v) * v2 * (pPrec->eta3 + 4.0*pPrec->eta3*Seff*v + - 2.0*pPrec->eta*(JNorm2 - pPrec->Spl2 + 2.0*(pPrec->S1_norm_2 - pPrec->S2_norm_2)*pPrec->delta_qq)*v2 + - 4.0*pPrec->eta*Seff*(JNorm2 - pPrec->Spl2)*v3 + (JNorm2 - pPrec->Spl2)*(JNorm2 - pPrec->Spl2)*v4*pPrec->inveta) ) + + // Equation B7 of Chatziioannou et al, PRD 95, 104004, (2017) + vout.y = JNorm * ( -1.5 * pPrec->eta * (pPrec->Spl2 - pPrec->Smi2)*(1.0 + 2.0*Seff*v - (JNorm2 - pPrec->Spl2)*v2*pPrec->inveta2) * (1.0 - Seff*v)*v4 ) + + // Equation B8 of Chatziioannou et al, PRD 95, 104004, (2017) + vout.z = JNorm * ( 0.75 * pPrec->inveta * (pPrec->Spl2 - pPrec->Smi2)*(pPrec->Spl2 - pPrec->Smi2)*(1.0 - Seff * v)*v6 ) + } + else + { + /* This is as implemented in LALSimInspiralFDPrecAngles, should be equivalent to above code. + c.f. https://git.ligo.org/lscsoft/lalsuite/-/blob/master/lalsimulation/lib/LALSimInspiralFDPrecAngles_internals.c#L578 + */ + double v_2 = v * v + double v_3 = v * v_2 + double v_4 = v_2 * v_2 + double v_6 = v_2 * v_4 + double J_norm = JNorm + double delta = pPrec->delta_qq + double eta = pPrec->eta + double eta_2 = eta * eta + + vout.x = -0.75*((JNorm2-pPrec->Spl2)*(JNorm2-pPrec->Spl2)*v_4/(pPrec->eta) - 4.*(pPrec->eta)*(pPrec->Seff)*(JNorm2-pPrec->Spl2)*v_3-2.*(JNorm2-pPrec->Spl2+2*((pPrec->S1_norm_2)-(pPrec->S2_norm_2))*(delta))*(pPrec->eta)*v_2+(4.*(pPrec->Seff)*v+1)*(pPrec->eta)*(eta_2)) *J_norm*v_2*((pPrec->Seff)*v-1.) + vout.y = 1.5*(pPrec->Smi2-pPrec->Spl2)*J_norm*((JNorm2-pPrec->Spl2)/(pPrec->eta)*v_2-2.*(pPrec->eta)*(pPrec->Seff)*v-(pPrec->eta))*((pPrec->Seff)*v-1.)*v_4 + vout.z = -0.75*J_norm*((pPrec->Seff)*v-1.)*(pPrec->Spl2-pPrec->Smi2)*(pPrec->Spl2-pPrec->Smi2)*v_6/(pPrec->eta) + } + + return vout + return None +def IMRPhenomX_Return_Constants_d_MSA(): + """ + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a311b7810fd9aff08158a20324384b5d6 + """ + return None def IMRPhenomX_costhetaLJ(L_norm: float, J_norm: float, S_norm: float): """ From b07bdf739150190854a802e1adcfb36881a20fc5 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 23 Sep 2025 14:39:39 +0200 Subject: [PATCH 012/474] Complete correction function in msa --- src/ripplegw/waveforms/msa.py | 74 +++++++++++++++++++---------------- 1 file changed, 41 insertions(+), 33 deletions(-) diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py index 280cffe5..9c95f461 100644 --- a/src/ripplegw/waveforms/msa.py +++ b/src/ripplegw/waveforms/msa.py @@ -32,7 +32,7 @@ def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime piGM = 1 pWF = None pPrec = None - v = freq * piGM * (2/mprime) ### v is the frequency. Not velocity. To be double checked + v = freq * piGM * (2/mprime) ### v is the frequency. Not velocity. To be checked v = v**(1/3) vangles = jax.vmap(lambda v_: IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v_, pWF, pPrec))(v) @@ -395,46 +395,54 @@ def IMRPhenomX_Return_Constants_c_MSA(v: float, JNorm: float, pPrec): JNorm2 = JNorm * JNorm - vector vout = {0.,0.,0.} + vout = jnp.array([0.0, 0.0, 0.0]) - Seff = pPrec->Seff + Seff = pPrec.Seff - if(pPrec->IMRPhenomXPrecVersion != 220) - { - // Equation B6 of Chatziioannou et al, PRD 95, 104004, (2017) - vout.x = JNorm * ( 0.75*(1.0 - Seff*v) * v2 * (pPrec->eta3 + 4.0*pPrec->eta3*Seff*v - - 2.0*pPrec->eta*(JNorm2 - pPrec->Spl2 + 2.0*(pPrec->S1_norm_2 - pPrec->S2_norm_2)*pPrec->delta_qq)*v2 - - 4.0*pPrec->eta*Seff*(JNorm2 - pPrec->Spl2)*v3 + (JNorm2 - pPrec->Spl2)*(JNorm2 - pPrec->Spl2)*v4*pPrec->inveta) ) + ### if ... else statement for pPrec.IMRPhenomXPrecVersion + + ### If terms + #Equation B6 of Chatziioannou et al, PRD 95, 104004, (2017) + x_not_220 = JNorm * ( 0.75*(1.0 - Seff*v) * v2 * (pPrec.eta3 + 4.0*pPrec.eta3*Seff*v + - 2.0*pPrec.eta*(JNorm2 - pPrec.Spl2 + 2.0*(pPrec.S1_norm_2 - pPrec.S2_norm_2)*pPrec.delta_qq)*v2 + - 4.0*pPrec.eta*Seff*(JNorm2 - pPrec.Spl2)*v3 + (JNorm2 - pPrec.Spl2)*(JNorm2 - pPrec.Spl2)*v4*pPrec.inveta) ) - // Equation B7 of Chatziioannou et al, PRD 95, 104004, (2017) - vout.y = JNorm * ( -1.5 * pPrec->eta * (pPrec->Spl2 - pPrec->Smi2)*(1.0 + 2.0*Seff*v - (JNorm2 - pPrec->Spl2)*v2*pPrec->inveta2) * (1.0 - Seff*v)*v4 ) + #Equation B7 of Chatziioannou et al, PRD 95, 104004, (2017) + y_not_220 = JNorm * ( -1.5 * pPrec.eta * (pPrec.Spl2 - pPrec.Smi2)*(1.0 + 2.0*Seff*v - (JNorm2 - pPrec.Spl2)*v2*pPrec.inveta2) * (1.0 - Seff*v)*v4 ) - // Equation B8 of Chatziioannou et al, PRD 95, 104004, (2017) - vout.z = JNorm * ( 0.75 * pPrec->inveta * (pPrec->Spl2 - pPrec->Smi2)*(pPrec->Spl2 - pPrec->Smi2)*(1.0 - Seff * v)*v6 ) - } - else - { - /* This is as implemented in LALSimInspiralFDPrecAngles, should be equivalent to above code. - c.f. https://git.ligo.org/lscsoft/lalsuite/-/blob/master/lalsimulation/lib/LALSimInspiralFDPrecAngles_internals.c#L578 - */ - double v_2 = v * v - double v_3 = v * v_2 - double v_4 = v_2 * v_2 - double v_6 = v_2 * v_4 - double J_norm = JNorm - double delta = pPrec->delta_qq - double eta = pPrec->eta - double eta_2 = eta * eta + #Equation B8 of Chatziioannou et al, PRD 95, 104004, (2017) + z_not_220 = JNorm * ( 0.75 * pPrec.inveta * (pPrec.Spl2 - pPrec.Smi2)*(pPrec.Spl2 - pPrec.Smi2)*(1.0 - Seff * v)*v6 ) + + #This is as implemented in LALSimInspiralFDPrecAngles, should be equivalent to above code. + #c.f. https://git.ligo.org/lscsoft/lalsuite/-/blob/master/lalsimulation/lib/LALSimInspiralFDPrecAngles_internals.c#L578 + + ### else terms + v_2 = v * v + v_3 = v * v_2 + v_4 = v_2 * v_2 + v_6 = v_2 * v_4 + J_norm = JNorm + delta = pPrec.delta_qq + eta = pPrec.eta + eta_2 = eta * eta - vout.x = -0.75*((JNorm2-pPrec->Spl2)*(JNorm2-pPrec->Spl2)*v_4/(pPrec->eta) - 4.*(pPrec->eta)*(pPrec->Seff)*(JNorm2-pPrec->Spl2)*v_3-2.*(JNorm2-pPrec->Spl2+2*((pPrec->S1_norm_2)-(pPrec->S2_norm_2))*(delta))*(pPrec->eta)*v_2+(4.*(pPrec->Seff)*v+1)*(pPrec->eta)*(eta_2)) *J_norm*v_2*((pPrec->Seff)*v-1.) - vout.y = 1.5*(pPrec->Smi2-pPrec->Spl2)*J_norm*((JNorm2-pPrec->Spl2)/(pPrec->eta)*v_2-2.*(pPrec->eta)*(pPrec->Seff)*v-(pPrec->eta))*((pPrec->Seff)*v-1.)*v_4 - vout.z = -0.75*J_norm*((pPrec->Seff)*v-1.)*(pPrec->Spl2-pPrec->Smi2)*(pPrec->Spl2-pPrec->Smi2)*v_6/(pPrec->eta) - } + x_else = -0.75*((JNorm2-pPrec.Spl2)*(JNorm2-pPrec.Spl2)*v_4/(pPrec.eta) - 4.*(pPrec.eta)*(pPrec.Seff)*(JNorm2-pPrec.Spl2)*v_3-2.*(JNorm2-pPrec.Spl2+2*((pPrec.S1_norm_2)-(pPrec.S2_norm_2))*(delta))*(pPrec.eta)*v_2+(4.*(pPrec.Seff)*v+1)*(pPrec.eta)*(eta_2)) *J_norm*v_2*((pPrec.Seff)*v-1.) + y_else = 1.5*(pPrec.Smi2-pPrec.Spl2)*J_norm*((JNorm2-pPrec.Spl2)/(pPrec.eta)*v_2-2.*(pPrec.eta)*(pPrec.Seff)*v-(pPrec.eta))*((pPrec.Seff)*v-1.)*v_4 + z_else = -0.75*J_norm*((pPrec.Seff)*v-1.)*(pPrec.Spl2-pPrec.Smi2)*(pPrec.Spl2-pPrec.Smi2)*v_6/(pPrec.eta) + + + x = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, x_not_220, x_else) + y = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, y_not_220, y_else) + z = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, z_not_220, z_else) + + + vout.at[0].set(x) + vout.at[1].set(y) + vout.at[2].set(z) - return vout + return vout - return None def IMRPhenomX_Return_Constants_d_MSA(): """ From 6fe82982a8cb0835f30bac70da8de23f3208106e Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 23 Sep 2025 14:50:08 +0200 Subject: [PATCH 013/474] Adjust MSA c and d functions --- src/ripplegw/waveforms/msa.py | 49 ++++++++++++++++------------------- 1 file changed, 23 insertions(+), 26 deletions(-) diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py index 9c95f461..e45ec9dc 100644 --- a/src/ripplegw/waveforms/msa.py +++ b/src/ripplegw/waveforms/msa.py @@ -266,19 +266,18 @@ def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, """ pflag = pPrec.IMRPhenomXPrecVersion v2 = v * v - vMSA = jnp.array([0., 0., 0.]) c_vec = IMRPhenomX_Return_Constants_c_MSA(v, JNorm, pPrec) d_vec = IMRPhenomX_Return_Constants_d_MSA(LNorm, JNorm, pPrec) - c0 = c_vec.x - c2 = c_vec.y - c4 = c_vec.z + c0 = c_vec[0] + c2 = c_vec[1] + c4 = c_vec[2] - d0 = d_vec.x - d2 = d_vec.y - d4 = d_vec.z + d0 = d_vec[0] + d2 = d_vec[1] + d4 = d_vec[2] #Pre-cache a bunch of useful variables two_d0 = 2.0 * d0 @@ -346,7 +345,8 @@ def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, ################# computing the x part of vMSA ##### vMSA_x = ( phiz_0_MSA_Cphi_term + phiz_0_MSA_Dphi_term ) - vMSA.at[0].set(vMSA_x) + vMSA_x = jnp.where(jnp.isnan(vMSA_x), 0.0, vMSA_x) ## check for NaN + ######## computing the y part of vMSA ##### ### The first MSA correction to \zeta as given in Eq. F19 @@ -355,16 +355,12 @@ def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, case_default = ( ( A_theta_L * (Cphi + Dphi) ) + (2.0 * d0 * B_theta_L) * ( ( Cphi / (sd - d2) ) - ( Dphi / (sd + d2) ) ) ) / psi_dot vMSA_y = jnp.where((pflag==222) | (pflag == 223) | (pflag==224), case_pflag, case_default) + vMSA_y = jnp.where(jnp.isnan(vMSA_y), 0.0, vMSA_y) ## check for NaN - vMSA.at[1].set(vMSA_y) - - - ################# checking for NaNs #################### - - vMSA = jnp.where(jnp.isnan(vMSA), 0.0, vMSA) - vMSA = vMSA.at[2].set(0.0) + # Obsolete component that we initialize to zero just in case + vMSA_z = 0.0 - return vMSA + return jnp.array([vMSA_x, vMSA_y, vMSA_z]) @@ -395,8 +391,6 @@ def IMRPhenomX_Return_Constants_c_MSA(v: float, JNorm: float, pPrec): JNorm2 = JNorm * JNorm - vout = jnp.array([0.0, 0.0, 0.0]) - Seff = pPrec.Seff ### if ... else statement for pPrec.IMRPhenomXPrecVersion @@ -434,21 +428,24 @@ def IMRPhenomX_Return_Constants_c_MSA(v: float, JNorm: float, pPrec): x = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, x_not_220, x_else) y = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, y_not_220, y_else) z = jnp.where(pPrec.IMRPhenomXPrecVersion!=220, z_not_220, z_else) - - - vout.at[0].set(x) - vout.at[1].set(y) - vout.at[2].set(z) - return vout + return jnp.array([x, y, z]) -def IMRPhenomX_Return_Constants_d_MSA(): +def IMRPhenomX_Return_Constants_d_MSA(LNorm: float, JNorm: float, pPrec): """ lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a311b7810fd9aff08158a20324384b5d6 """ - return None + + LNorm2 = LNorm*LNorm + JNorm2 = JNorm*JNorm + + x = -( JNorm2 - (LNorm + pPrec.Spl)*(LNorm + pPrec.Spl)) * ( (JNorm2 - (LNorm - pPrec.Spl)*(LNorm - pPrec.Spl)) ) + y = -2.0*(pPrec.Spl2 - pPrec.Smi2)*(JNorm2 + LNorm2 - pPrec.Spl2) + z = -(pPrec.Spl2 - pPrec.Smi2)*(pPrec.Spl2 - pPrec.Smi2) + + return jnp.array([x, y, z]) def IMRPhenomX_costhetaLJ(L_norm: float, J_norm: float, S_norm: float): """ From 9b7635850aa8c466202896f9c30d70638cda1a69 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 23 Sep 2025 17:28:38 +0200 Subject: [PATCH 014/474] Update pPrec structure --- src/ripplegw/waveforms/PrecessionStruct.py | 203 +++++++++++++++++++++ src/ripplegw/waveforms/msa.py | 52 ++---- 2 files changed, 222 insertions(+), 33 deletions(-) create mode 100644 src/ripplegw/waveforms/PrecessionStruct.py diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py new file mode 100644 index 00000000..b6f0c027 --- /dev/null +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -0,0 +1,203 @@ +import jax.numpy as jnp +import math +from ..typing import Array +from ..constants import G, MSUN, C + + +class IMRPhenomXGetAndSetPrecessionVariables: + + def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, lalParams, debug_flag): + """ + lalsuite: https:#lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#af089ef2586c52b12016c0d791b176121 + + Functions to Perform Frame Transformations and Populate Structs + - Calculates frame transformation + - PN Euler angles + - Frame transformations + - Orbital angular momenta etc + + + pWF: Some useful waveform parameters + + m1_SI: Mass of object 1 (heavier one) in SI units + m2_SI: Mass of object 2 (lighter one) in SI units + + + chi1x: Object 1 spin. x-component. + chi1y: Object 1 spin. y-component. + chi1z: Object 1 spin. z-component. + + chi2x: Object 2 spin. x-component. + chi2y: Object 2 spin. y-component. + chi2z: Object 2 spin. z-component. + + lalParams: FIXME + debug_flag: FIXME + """ + + # Here we assume m1 > m2, q > 1, dm = m1 - m2 = delta = sqrt(1-4eta) > 0 + self.pWF = pWF + + self.IMRPhenomXPrecVersion = lalParams['IMRPhenomXPrecVersion'] + + ## default to NNLO angles if in-plane spins are negligible and one of the SpinTaylor options has been selected. The solutions would be dominated by numerical noise. + self.IMRPhenomXPrecVersion = jnp.where(self.IMRPhenomXPrecVersion == 300, 223, self.IMRPhenomXPrecVersion) + chi_in_plane = jnp.sqrt(chi1x*chi1x+chi1y*chi1y+chi2x*chi2x+chi2y*chi2y) + self.IMRPhenomXPrecVersion = jnp.where((chi_in_plane<1e-6) & (self.IMRPhenomXPrecVersion == 330), 102, self.IMRPhenomXPrecVersion) + + cond1 = chi_in_plane<1e-7 + cond2 = (self.IMRPhenomXPrecVersion==320) | (self.IMRPhenomXPrecVersion==321) | (self.IMRPhenomXPrecVersion==310) | (self.IMRPhenomXPrecVersion==311) + self.IMRPhenomXPrecVersion = jnp.where((cond1) & (cond2), 102, self.IMRPhenomXPrecVersion) + + + self.ExpansionOrder = lalParams['ExpansionOrder'] + self.PNRUseTunedAngles = lalParams['PNRUseTunedAngles'] + self.IMRPhenomXPNRInterpTolerance = lalParams['IMRPhenomXPNRInterpTolerance'] + self.AntisymmetricWaveform = lalParams['AntisymmetricWaveform'] + self.PolarizationSymmetry = 1.0 + + + + ########## Define a number of convenient local parameters ############# + m1 = m1_SI / pWF['Mtot_SI'] #/* Normalized mass of larger companion: m1_SI / Mtot_SI */ + m2 = m2_SI / pWF['Mtot_SI'] #/* Normalized mass of smaller companion: m2_SI / Mtot_SI */ + M = (m1 + m2) #/* Total mass in solar units */ + + # Useful powers of mass + m1_2 = m1 * m1 + m1_3 = m1 * m1_2 + m1_4 = m1 * m1_3 + m1_5 = m1 * m1_4 + m1_6 = m1 * m1_5 + m1_7 = m1 * m1_6 + m1_8 = m1 * m1_7 + + m2_2 = m2 * m2 + + pWF['M'] = M + pWF['m1_2'] = m1_2 + pWF['m2_2'] = m2_2 + + q = m1/m2 + + + # Powers of eta + eta = pWF['eta'] + eta2 = eta*eta + eta3 = eta*eta2 + eta4 = eta*eta3 + eta5 = eta*eta4 + eta6 = eta*eta5 + + # \delta in terms of q > 1 + delta = pWF['delta'] + delta2 = delta*delta + delta3 = delta*delta2 + + # Cache these powers, as we use them regularly + self.eta = eta + self.eta2 = eta2 + self.eta3 = eta3 + self.eta4 = eta4 + + self.inveta = 1.0 / eta + self.inveta2 = 1.0 / eta2 + self.inveta3 = 1.0 / eta3 + self.inveta4 = 1.0 / eta4 + self.sqrt_inveta = 1.0 / jnp.sqrt(eta) + + chi_eff = pWF['chiEff'] + + self.twopiGM = 2*jnp.pi * G * (m1_SI + m2_SI) / C / C / C + self.piGM = jnp.pi * (m1_SI + m2_SI) * (G / C) / (C * C) + + #### Set spin variables in pPrec struct ###### + self.chi1x = chi1x + self.chi1y = chi1y + self.chi1z = chi1z + self.chi1_norm = jnp.sqrt(chi1x*chi1x + chi1y*chi1y + chi1z*chi1z) + + self.chi2x = chi2x + self.chi2y = chi2y + self.chi2z = chi2z + self.chi2_norm = jnp.sqrt(chi2x*chi2x + chi2y*chi2y + chi2z*chi2z) + + ### /* Check that spins obey Kerr bound */ #### + """ + FIXME + ### I will come back to the Kerr bound later line 210 ############ + + condition = (jnp.logical_not(self.PNRUseTunedAngles)) | (pWF['PNR_SINGLE_SPIN'] != 1) + + if condition: + kerr_boud_1 = jnp.abs(self.chi1_norm) <= 1.0 + kerr_boud_2 = jnp.abs(self.chi2_norm) <= 1.0 + if kerr_boud_1 & kerr_boud_2: + Continue running + else: + Quit + print("Error in IMRPhenomXSetPrecessionVariables: |S1/m1^2| must be <= 1.\n") + else: + continue running + """ + + ###/* Calculate dimensionful spins */ + self.S1x = chi1x * m1_2 + self.S1y = chi1y * m1_2 + self.S1z = chi1z * m1_2 + self.S1_norm = jnp.abs(self.chi1_norm) * m1_2 + + self.S2x = chi2x * m2_2 + self.S2y = chi2y * m2_2 + self.S2z = chi2z * m2_2 + self.S2_norm = jnp.abs(self.chi2_norm) * m2_2 + + ###// Useful powers + self.S1_norm_2 = self.S1_norm * self.S1_norm + self.S2_norm_2 = self.S2_norm * self.S2_norm + + self.chi1_perp = jnp.sqrt(chi1x*chi1x + chi1y*chi1y) + self.chi2_perp = jnp.sqrt(chi2x*chi2x + chi2y*chi2y) + + ###/* Get spin projections */ + self.S1_perp = (m1_2) * jnp.sqrt(chi1x*chi1x + chi1y*chi1y) + self.S2_perp = (m2_2) * jnp.sqrt(chi2x*chi2x + chi2y*chi2y) + + ###/* Norm of in-plane vector sum: Norm[ S1perp + S2perp ] */ + self.STot_perp = jnp.sqrt( (self.S1x+self.S2x)*(self.S1x+self.S2x) + (self.S1y+self.S2y)*(self.S1y+self.S2y) ) + + ##/* This is called chiTot_perp to distinguish from Sperp used in contrusction of chi_p. For normalization, see Sec. IV D of arXiv:2004.06503 */ + self.chiTot_perp = self.STot_perp * (M*M) / m1_2 + ##/* Store self.chiTot_perp to pWF so that it can be used in XCP modifications (PNRUseTunedCoprec) */ + pWF['chiTot_perp'] = self.chiTot_perp + + + self.PNRUseTunedAngles, lalParams['PNRUseTunedAngles'], self.AntisymmetricWaveform, lalParams['AntisymmetricWaveform'], lalParams['PNRUseTunedCoprec'] = jnp.where((chi_in_plane<1e-7) & (self.PNRUseTunedAngles == 1), jnp.array([False, False, False, False, False]), jnp.array([self.PNRUseTunedAngles, lalParams['PNRUseTunedAngles'], self.AntisymmetricWaveform, lalParams['AntisymmetricWaveform'], lalParams['PNRUseTunedCoprec']])) + + + ### Implementation up to line 257 + + + + """ + self.eta = eta + self.Omegazeta0_coeff = Omegazeta0_coeff + self.Omegazeta1_coeff = Omegazeta1_coeff + self.Omegazeta2_coeff = Omegazeta2_coeff + self.Omegazeta3_coeff = Omegazeta3_coeff + self.Omegazeta4_coeff = Omegazeta4_coeff + self.Omegazeta5_coeff = Omegazeta5_coeff + self.zeta_0 = zeta_0 + """ + + + + + +def assert_int(condition: bool, success: int = 0, failure: int = -1) -> int: + """ + Returns `success` if condition is True, otherwise `failure`. + + Equivalent to a C macro that checks an assertion and returns -1 on failure. + """ + return jnp.where(condition, success, failure) \ No newline at end of file diff --git a/src/ripplegw/waveforms/msa.py b/src/ripplegw/waveforms/msa.py index e45ec9dc..3aea390b 100644 --- a/src/ripplegw/waveforms/msa.py +++ b/src/ripplegw/waveforms/msa.py @@ -11,12 +11,16 @@ gsl_sf_elljac_e This does not exist in numpy or jax.scipy.special. + +FIXME: Check the use of jnp.arctan """ def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime: int): ## XLALSimIMRPhenomXPMSAAngles ''' + lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/group___l_a_l_sim_i_m_r_phenom_x__c.html#ga66f87de36e00f98e62a04042f918a921 + lalsim.SimIMRPhenomXPMSAAngles(f_seq, params['mass_1'] * lal.MSUN_SI, params['mass_2'] * lal.MSUN_SI, @@ -38,9 +42,9 @@ def XLALSimIMRPhenomXPMSAAngles(freq: Array, params: Array, f_ref: float, mprime - alpha_of_f = vangles.x - pPrec.alpha_offset - gamma_of_f = -1 * (vangles.y - pPrec.epsilon_offset) - cosbeta_of_f = vangles.z + alpha_of_f = vangles[0] - pPrec.alpha_offset + gamma_of_f = -1 * (vangles[1] - pPrec.epsilon_offset) + cosbeta_of_f = vangles[2] return alpha_of_f, gamma_of_f, cosbeta_of_f @@ -54,8 +58,6 @@ def IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v: Array, pWF, pPrec): pPrec: IMRPhenomX precession struct """ - vout = {0.,0.,0.} - L_norm = pWF.eta / v J_norm = IMRPhenomX_JNorm_MSA(L_norm, pPrec) @@ -73,9 +75,9 @@ def IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v: Array, pWF, pPrec): vRoots = IMRPhenomX_Return_Roots_MSA(L_norm,J_norm,pPrec) - pPrec.S32 = vRoots.x - pPrec.Smi2 = vRoots.y - pPrec.Spl2 = vRoots.z + pPrec.S32 = vRoots[0] + pPrec.Smi2 = vRoots[1] + pPrec.Spl2 = vRoots[2] ### define following four from the three above pPrec.Spl2mSmi2 @@ -94,8 +96,8 @@ def IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v: Array, pWF, pPrec): - phiz_MSA = vMSA.x - zeta_MSA = vMSA.y + phiz_MSA = vMSA[0] + zeta_MSA = vMSA[1] phiz = IMRPhenomX_Return_phiz_MSA(v,J_norm,pPrec) zeta = IMRPhenomX_Return_zeta_MSA(v,pPrec) @@ -106,7 +108,7 @@ def IMRPhenomX_Return_phi_zeta_costhetaL_MSA(v: Array, pWF, pPrec): vout_z = cos_theta_L - return vout_x, vout_y, vout_z + return jnp.array([vout_x, vout_y, vout_z]) def IMRPhenomX_JNorm_MSA(LNorm: float, pPrec): @@ -122,7 +124,6 @@ def IMRPhenomX_Return_Roots_MSA(LNorm: float, JNorm: float, pPrec): """ lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a3d6fc6f6edff39a14e884521a3cd2997 """ - vout = None tmp1 = 0.0 tmp2 = 0.0 @@ -133,9 +134,9 @@ def IMRPhenomX_Return_Roots_MSA(LNorm: float, JNorm: float, pPrec): vBCD = IMRPhenomX_Return_Spin_Evolution_Coefficients_MSA(LNorm, JNorm, pPrec) - B = vBCD.x - C = vBCD.y - D = vBCD.z + B = vBCD[0] + C = vBCD[1] + D = vBCD[2] S1Norm2 = pPrec.S1_norm_2 S2Norm2 = pPrec.S2_norm_2 @@ -186,7 +187,7 @@ def IMRPhenomX_Return_Roots_MSA(LNorm: float, JNorm: float, pPrec): Smi2 = jnp.where(condition, S0Norm2, tmp6) Spl2 = jnp.where(condition, Smi2 + 1e-9, tmp4) - return S32, Smi2, Spl2 + return jnp.array([S32, Smi2, Spl2]) @@ -215,7 +216,7 @@ def IMRPhenomX_Return_Spin_Evolution_Coefficients_MSA(LNorm: float, JNorm: float vout_y = J2mL2Sq - 2.0*LNorm*Seff*J2mL2 - 2.0*((1.0 - q)/q)*LNorm2*(S1Norm2 - q*S2Norm2) + 4.0*eta*LNorm2*Seff*Seff - 2.0*delta*(S1Norm2 - S2Norm2)*Seff*LNorm + 2.0*((1.0 - q)/q)*(q*S1Norm2 - S2Norm2)*JNorm2 vout_z = ((1.0 - q)/q)*(S2Norm2 - q*S1Norm2)*J2mL2Sq + deltaSq*(S1Norm2 - S2Norm2)*(S1Norm2 - S2Norm2)*LNorm2/eta + 2.0*delta*LNorm*Seff*(S1Norm2 - S2Norm2)*J2mL2 - return vout_x, vout_y, vout_z + return jnp.array([vout_x, vout_y, vout_z]) def IMRPhenomX_L_norm_3PN_of_v(v: float, v2: float, L_norm: float, pPrec): @@ -351,7 +352,7 @@ def IMRPhenomX_Return_MSA_Corrections_MSA(v: float, LNorm: float, JNorm: float, ######## computing the y part of vMSA ##### ### The first MSA correction to \zeta as given in Eq. F19 - case_pflag = A_theta_L*vMSA.x + 2.*B_theta_L*d0*(phiz_0_MSA_Cphi_term/(sd-d2) - phiz_0_MSA_Dphi_term/(sd+d2)) + case_pflag = A_theta_L*vMSA_x + 2.*B_theta_L*d0*(phiz_0_MSA_Cphi_term/(sd-d2) - phiz_0_MSA_Dphi_term/(sd+d2)) case_default = ( ( A_theta_L * (Cphi + Dphi) ) + (2.0 * d0 * B_theta_L) * ( ( Cphi / (sd - d2) ) - ( Dphi / (sd + d2) ) ) ) / psi_dot vMSA_y = jnp.where((pflag==222) | (pflag == 223) | (pflag==224), case_pflag, case_default) @@ -432,7 +433,6 @@ def IMRPhenomX_Return_Constants_c_MSA(v: float, JNorm: float, pPrec): return jnp.array([x, y, z]) - def IMRPhenomX_Return_Constants_d_MSA(LNorm: float, JNorm: float, pPrec): """ lalsuite: https://lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#a311b7810fd9aff08158a20324384b5d6 @@ -534,17 +534,3 @@ def IMRPhenomX_Return_phiz_MSA(v: Array, JNorm, pPrec): -class IMRPhenomXPrecessionStruct: - def __init__(self, eta, Omegazeta0_coeff, Omegazeta1_coeff, Omegazeta2_coeff, - Omegazeta3_coeff, Omegazeta4_coeff, Omegazeta5_coeff, zeta_0): - self.eta = eta - self.Omegazeta0_coeff = Omegazeta0_coeff - self.Omegazeta1_coeff = Omegazeta1_coeff - self.Omegazeta2_coeff = Omegazeta2_coeff - self.Omegazeta3_coeff = Omegazeta3_coeff - self.Omegazeta4_coeff = Omegazeta4_coeff - self.Omegazeta5_coeff = Omegazeta5_coeff - self.zeta_0 = zeta_0 - - - From 8c7648284825c641820bfe7f176080e9d3579034 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Wed, 24 Sep 2025 19:09:56 +0200 Subject: [PATCH 015/474] Edits to the pPrec --- src/ripplegw/waveforms/PrecessionStruct.py | 157 ++++++++++++++++----- 1 file changed, 121 insertions(+), 36 deletions(-) diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py index b6f0c027..d97082c8 100644 --- a/src/ripplegw/waveforms/PrecessionStruct.py +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -2,11 +2,11 @@ import math from ..typing import Array from ..constants import G, MSUN, C - +import jax class IMRPhenomXGetAndSetPrecessionVariables: - def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, lalParams, debug_flag): + def __init__(self, pWF: dict, m1_SI: float, m2_SI: float, chi1x: float, chi1y: float, chi1z: float, chi2x: float, chi2y: float, chi2z: float, lalParams: dict, debug_flag: bool): """ lalsuite: https:#lscsoft.docs.ligo.org/lalsuite/lalsimulation/_l_a_l_sim_i_m_r_phenom_x__precession_8c.html#af089ef2586c52b12016c0d791b176121 @@ -37,6 +37,8 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, # Here we assume m1 > m2, q > 1, dm = m1 - m2 = delta = sqrt(1-4eta) > 0 self.pWF = pWF + self.pWF['LALparams'] = lalParams + self.debug_prec = debug_flag self.IMRPhenomXPrecVersion = lalParams['IMRPhenomXPrecVersion'] @@ -56,11 +58,11 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, self.AntisymmetricWaveform = lalParams['AntisymmetricWaveform'] self.PolarizationSymmetry = 1.0 - + #### Skipping the multibanding bookkeeping ########## Define a number of convenient local parameters ############# - m1 = m1_SI / pWF['Mtot_SI'] #/* Normalized mass of larger companion: m1_SI / Mtot_SI */ - m2 = m2_SI / pWF['Mtot_SI'] #/* Normalized mass of smaller companion: m2_SI / Mtot_SI */ + m1 = m1_SI / self.pWF['Mtot_SI'] #/* Normalized mass of larger companion: m1_SI / Mtot_SI */ + m2 = m2_SI / self.pWF['Mtot_SI'] #/* Normalized mass of smaller companion: m2_SI / Mtot_SI */ M = (m1 + m2) #/* Total mass in solar units */ # Useful powers of mass @@ -74,15 +76,15 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, m2_2 = m2 * m2 - pWF['M'] = M - pWF['m1_2'] = m1_2 - pWF['m2_2'] = m2_2 + self.pWF['M'] = M + self.pWF['m1_2'] = m1_2 + self.pWF['m2_2'] = m2_2 q = m1/m2 # Powers of eta - eta = pWF['eta'] + eta = self.pWF['eta'] eta2 = eta*eta eta3 = eta*eta2 eta4 = eta*eta3 @@ -90,7 +92,7 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, eta6 = eta*eta5 # \delta in terms of q > 1 - delta = pWF['delta'] + delta = self.pWF['delta'] delta2 = delta*delta delta3 = delta*delta2 @@ -106,7 +108,7 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, self.inveta4 = 1.0 / eta4 self.sqrt_inveta = 1.0 / jnp.sqrt(eta) - chi_eff = pWF['chiEff'] + chi_eff = self.pWF['chiEff'] self.twopiGM = 2*jnp.pi * G * (m1_SI + m2_SI) / C / C / C self.piGM = jnp.pi * (m1_SI + m2_SI) * (G / C) / (C * C) @@ -130,15 +132,8 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, condition = (jnp.logical_not(self.PNRUseTunedAngles)) | (pWF['PNR_SINGLE_SPIN'] != 1) if condition: - kerr_boud_1 = jnp.abs(self.chi1_norm) <= 1.0 - kerr_boud_2 = jnp.abs(self.chi2_norm) <= 1.0 - if kerr_boud_1 & kerr_boud_2: - Continue running - else: - Quit - print("Error in IMRPhenomXSetPrecessionVariables: |S1/m1^2| must be <= 1.\n") - else: - continue running + assert jnp.abs(self.chi1_norm) <= 1.0 + assert jnp.abs(self.chi2_norm) <= 1.0 """ ###/* Calculate dimensionful spins */ @@ -169,35 +164,125 @@ def __init__(self, pWF, m1_SI, m2_SI, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, ##/* This is called chiTot_perp to distinguish from Sperp used in contrusction of chi_p. For normalization, see Sec. IV D of arXiv:2004.06503 */ self.chiTot_perp = self.STot_perp * (M*M) / m1_2 ##/* Store self.chiTot_perp to pWF so that it can be used in XCP modifications (PNRUseTunedCoprec) */ - pWF['chiTot_perp'] = self.chiTot_perp + self.pWF['chiTot_perp'] = self.chiTot_perp self.PNRUseTunedAngles, lalParams['PNRUseTunedAngles'], self.AntisymmetricWaveform, lalParams['AntisymmetricWaveform'], lalParams['PNRUseTunedCoprec'] = jnp.where((chi_in_plane<1e-7) & (self.PNRUseTunedAngles == 1), jnp.array([False, False, False, False, False]), jnp.array([self.PNRUseTunedAngles, lalParams['PNRUseTunedAngles'], self.AntisymmetricWaveform, lalParams['AntisymmetricWaveform'], lalParams['PNRUseTunedCoprec']])) - ### Implementation up to line 257 + # Calculate the effective precessing spin parameter (Schmidt et al, PRD 91, 024043, 2015): m1 > m2, so body 1 is the larger black hole + self.A1 = 2.0 + (3.0 * m2) / (2.0 * m1) + self.A2 = 2.0 + (3.0 * m1) / (2.0 * m2) + self.ASp1 = self.A1 * self.S1_perp + self.ASp2 = self.A2 * self.S2_perp + + #/* S_p = max(A1 S1_perp, A2 S2_perp) */ + num = jnp.where(self.ASp2 > self.ASp1, self.ASp2, self.ASp1) + den = jnp.where(m2 > m1 , self.A2*m2_2, self.A1*m1_2) + + #/* chi_p = max(A1 * Sp1 , A2 * Sp2) / (A_i * m_i^2) where i is the index of the larger BH */ + chip = num / den + chi1L = chi1z + chi2L = chi2z + + + self.chi_p = chip + #// (PNRUseTunedCoprec) + self.pWF['chi_p'] = self.chi_p + self.phi0_aligned = self.pWF['phi0'] + + #/* Effective (dimensionful) aligned spin */ + self.SL = chi1L*m1_2 + chi2L*m2_2 + + #/* Effective (dimensionful) in-plane spin */ + self.Sperp = chip * m1_2 # /* m1 > m2 */ + + self.MSA_ERROR = 0 + + self.pWF22AS = None + + + #// get first digit of precessing version: this tags the method employed to compute the Euler angles + #// 1: NNLO 2: MSA 3: SpinTaylor (numerical) + precversionTag = (self.IMRPhenomXPrecVersion-(self.IMRPhenomXPrecVersion%100))/100 + precversionTag = jnp.int32(precversionTag) + #/* start of SpinTaylor code */ + + + ##################################### """ - self.eta = eta - self.Omegazeta0_coeff = Omegazeta0_coeff - self.Omegazeta1_coeff = Omegazeta1_coeff - self.Omegazeta2_coeff = Omegazeta2_coeff - self.Omegazeta3_coeff = Omegazeta3_coeff - self.Omegazeta4_coeff = Omegazeta4_coeff - self.Omegazeta5_coeff = Omegazeta5_coeff - self.zeta_0 = zeta_0 + if precversionTag==3: + self.PNarrays = {} + self.L_MAX_PNR = self.M_MAX ### Not defined before. M_MAX? + ModeArray = lalParams['ModeArray'] + LMAX_PNR = 2 + if ModeArray is not None: + if (4, 4) in ModeArray: + LMAX_PNR = 4 + elif (3, 3) or (3, 2) in ModeArray: + LMAX_PNR = 3 """ - + + flow = self.compute_flow() + + def compute_flow(self)->float: + """ + Substitute function for line 324-340 in LALSimIMRPhenomX_precession.c script + """ + + def PNRTuned_true(_): + + return jnp.where(self.pWF['deltaF']==0.0, self.pWF['fMin'], jnp.floor_divide(self.pWF['fMin'], self.pWF['deltaF'])*self.pWF['deltaF']) + + def PNRTuned_false(_): + self.M_MAX = 1.0 #FIXME this is definietly not the right value + self.integration_buffer = jnp.where(self.pWF['deltaF']>0.0, 3*self.pWF['deltaF'], 0.5) + return (self.pWF['fMin'] - self.integration_buffer)*2 / self.M_MAX + + return jax.lax.cond(self.PNRUseTunedAngles, PNRTuned_true, PNRTuned_false, operand = None) -def assert_int(condition: bool, success: int = 0, failure: int = -1) -> int: +''' +def get_deltaF_from_wfstruct(pWF: dict): """ - Returns `success` if condition is True, otherwise `failure`. - - Equivalent to a C macro that checks an assertion and returns -1 on failure. + To be tested the jnp functions + """ - return jnp.where(condition, success, failure) \ No newline at end of file + seglen=XLALSimInspiralChirpTimeBound(pWF['fRef'], pWF['m1_SI'], pWF['m2_SI'], pWF['chi1L'],pWF['chi2L']) + deltaFv1= 1./jnp.max(4.,jnp.pow(2, jnp.ceil(jnp.log(seglen)/jnp.log(2)))) + deltaF = jnp.min(deltaFv1,0.1) + deltaMF = XLALSimIMRPhenomXUtilsHztoMf(deltaF,pWF['Mtot']) + return deltaMF +''' + +def XLALSimIMRPhenomXUtilsHztoMf(): + return None + +def XLALSimInspiralChirpTimeBound(): + + + return None + + + + + + + + + + + + + + + + + + + From 5b3e1be133875860440f1749f4d85432f5af79d8 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Fri, 26 Sep 2025 17:06:49 +0200 Subject: [PATCH 016/474] Update the L_MAX choice; up to line 410 --- src/ripplegw/waveforms/PrecessionStruct.py | 60 +++++++++++++--------- 1 file changed, 37 insertions(+), 23 deletions(-) diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py index d97082c8..ebb32acb 100644 --- a/src/ripplegw/waveforms/PrecessionStruct.py +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -37,7 +37,8 @@ def __init__(self, pWF: dict, m1_SI: float, m2_SI: float, chi1x: float, chi1y: f # Here we assume m1 > m2, q > 1, dm = m1 - m2 = delta = sqrt(1-4eta) > 0 self.pWF = pWF - self.pWF['LALparams'] = lalParams + #self.pWF['LALparams'] = lalParams + self.lalParams = lalParams self.debug_prec = debug_flag self.IMRPhenomXPrecVersion = lalParams['IMRPhenomXPrecVersion'] @@ -213,29 +214,42 @@ def __init__(self, pWF: dict, m1_SI: float, m2_SI: float, chi1x: float, chi1y: f ##################################### - """ - if precversionTag==3: - self.PNarrays = {} - self.L_MAX_PNR = self.M_MAX ### Not defined before. M_MAX? - ModeArray = lalParams['ModeArray'] - LMAX_PNR = 2 - if ModeArray is not None: - if (4, 4) in ModeArray: - LMAX_PNR = 4 - elif (3, 3) or (3, 2) in ModeArray: - LMAX_PNR = 3 - """ + precversionTag_3_true = None + precversionTag_3_true = False + self.precversionTag3() + + + + ### up to line 405 + + def precversionTag3(self)->None: + + self.L_MAX_PNR = jnp.max(self.lalParams['ModeArray']) + self.ModeArray = self.lalParams['ModeArray'] + + #self.pWF['deltaMF'] = get_deltaF_from_wfstruct(self.pWF) #FIXME flow = self.compute_flow() - + assert flow>0. + + self.PNarrays, self.fmin_integration = IMRPhenomX_InspiralAngles_SpinTaylor(self.chi1x, self.chi1y, self.chi1z, self.chi2x, self.chi2y, self.chi2z, flow, self.IMRPhenomXPrecVersion, self.pWF, self.lalParams) + + self.Mfmin_integration = XLALSimIMRPhenomXUtilsHztoMf(self.fmin_integration, self.pWF['Mtot']) + + + + + + + return None + def compute_flow(self)->float: """ Substitute function for line 324-340 in LALSimIMRPhenomX_precession.c script """ def PNRTuned_true(_): - return jnp.where(self.pWF['deltaF']==0.0, self.pWF['fMin'], jnp.floor_divide(self.pWF['fMin'], self.pWF['deltaF'])*self.pWF['deltaF']) def PNRTuned_false(_): @@ -247,18 +261,17 @@ def PNRTuned_false(_): -''' def get_deltaF_from_wfstruct(pWF: dict): """ To be tested the jnp functions - """ - seglen=XLALSimInspiralChirpTimeBound(pWF['fRef'], pWF['m1_SI'], pWF['m2_SI'], pWF['chi1L'],pWF['chi2L']) - deltaFv1= 1./jnp.max(4.,jnp.pow(2, jnp.ceil(jnp.log(seglen)/jnp.log(2)))) - deltaF = jnp.min(deltaFv1,0.1) - deltaMF = XLALSimIMRPhenomXUtilsHztoMf(deltaF,pWF['Mtot']) + #seglen=XLALSimInspiralChirpTimeBound(pWF['fRef'], pWF['m1_SI'], pWF['m2_SI'], pWF['chi1L'],pWF['chi2L']) + #deltaFv1= 1./jnp.max(4.,jnp.pow(2, jnp.ceil(jnp.log(seglen)/jnp.log(2)))) + #deltaF = jnp.min(deltaFv1,0.1) + #deltaMF = XLALSimIMRPhenomXUtilsHztoMf(deltaF,pWF['Mtot']) + deltaMF = None return deltaMF -''' + def XLALSimIMRPhenomXUtilsHztoMf(): return None @@ -269,7 +282,8 @@ def XLALSimInspiralChirpTimeBound(): return None - +def IMRPhenomX_InspiralAngles_SpinTaylor(): + return None From ea565063400ffb47edc6e812d2ace44b9ada909b Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Mon, 29 Sep 2025 16:22:03 +0200 Subject: [PATCH 017/474] Update IMRPhenomX_InspiralAngles_SpinTaylor function --- src/ripplegw/waveforms/PrecessionStruct.py | 97 ++++++++++++++++++++-- 1 file changed, 91 insertions(+), 6 deletions(-) diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py index ebb32acb..d371b974 100644 --- a/src/ripplegw/waveforms/PrecessionStruct.py +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -237,10 +237,6 @@ def precversionTag3(self)->None: self.Mfmin_integration = XLALSimIMRPhenomXUtilsHztoMf(self.fmin_integration, self.pWF['Mtot']) - - - - return None @@ -282,12 +278,99 @@ def XLALSimInspiralChirpTimeBound(): return None -def IMRPhenomX_InspiralAngles_SpinTaylor(): - return None +def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: float, + chi2x: float, chi2y: float, chi2z: float, + fmin: float, PrecVersion: int, pWF: dict, lalParams: dict): + + + + fRef = pWF['fRef'] + m1_SI = pWF['m1_SI'] + m2_SI = pWF['m2_SI'] + + s1x=chi1x + s1y=chi1y + s1z=chi1z + + s2x=chi2x + s2y=chi2y + s2z=chi2z + + # Unused piGM = jnp.pi * (pWF['m1_SI'] + pWF['m2_SI']) * (G / C) / (C * C) + + + quadparam1=pWF["quadparam1"] + quadparam2=pWF["quadparam2"] + lambda1=pWF["lambda1"] + lambda2=pWF["lambda2"] + + PrecVersion_cond = (PrecVersion==311) | (PrecVersion==321) + quadparam1 = jnp.where(PrecVersion_cond, 1, quadparam1) + quadparam2 = jnp.where(PrecVersion_cond, 1, quadparam2) + lambda1 = jnp.where(PrecVersion_cond, 0, lambda1) + lambda2 = jnp.where(PrecVersion_cond, 0, lambda2) + + phaseO = jnp.where(lalParams['phaseO']==-1, 7, lalParams['phaseO']) + spinO = jnp.where(lalParams['spinO']==-1, 6, lalParams['spinO']) + tideO = jnp.where(lalParams['tideO']==-1, 12, lalParams['tideO']) + + + approx = lalParams['approx_name'] + + fMECO_Hz = XLALSimIMRPhenomXUtilsMftoHz(pWF['fMECO'], pWF['Mtot']) + + fmin = jax.lax.select((fmin > fMECO_Hz) & ((PrecVersion==320) | (PrecVersion==321)), fMECO_Hz, fmin) + + fCut = XLALSimIMRPhenomXUtilsMftoHz(pWF['fRING']+8 * pWF['fDAMP'], pWF['Mtot']) + + deltaT_coarse = .5 * lalParams['coarse_fac'] / fCut + + + + + + + + + FIX = jax.lax.select(True, fRef_equal_to_fmin, fRef_greater_than_fmin) + + if lalParams['coarse_fac'] > 1: + 'Do something' + + else: + 'Do something else' + + "Done!" + PhenomXPInspiralArrays = None + return PhenomXPInspiralArrays, 0 +def fRef_equal_to_fmin(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx): + V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) + return V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z + +def fRef_greater_than_fmin(fRef, fmin, deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx): + fS = fRef + fE = fmin - 0.5 + + V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) + + if len(V['data']) > 1: + V2, Phi2, S1x2, S1y2, S1z2, S2x2, S2y2, S2z2, LNhatx2, LNhaty2, LNhatz2, E1x2, E1y2, E1z2, = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, + m1_SI, m2_SI, fS, fE, s1x, s1y, s1z, s2x, s2y, + s2z, lnhatx, lnhaty, lnhatz, e1x, e1y, e1z, lambda1,lambda2, quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) + ### append all ### + else: + ### destroy all + pass + + return V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z + + +def XLALSimInspiralSpinTaylorPNEvolveOrbit(): + return None @@ -300,3 +383,5 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(): +def XLALSimIMRPhenomXUtilsMftoHz(): + return None \ No newline at end of file From 972dfb0a1b7f49de85f3f554fa3924be1596c299 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 7 Oct 2025 17:38:43 +0200 Subject: [PATCH 018/474] Add precession --- src/ripplegw/waveforms/PrecessionStruct.py | 127 ++++++++++++++++++--- 1 file changed, 114 insertions(+), 13 deletions(-) diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py index d371b974..003ea203 100644 --- a/src/ripplegw/waveforms/PrecessionStruct.py +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -296,7 +296,9 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa s2y=chi2y s2z=chi2z - # Unused piGM = jnp.pi * (pWF['m1_SI'] + pWF['m2_SI']) * (G / C) / (C * C) + + + piGM = jnp.pi * (pWF['m1_SI'] + pWF['m2_SI']) * (G / C) / (C * C) quadparam1=pWF["quadparam1"] @@ -314,6 +316,14 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa spinO = jnp.where(lalParams['spinO']==-1, 6, lalParams['spinO']) tideO = jnp.where(lalParams['tideO']==-1, 12, lalParams['tideO']) + lnhatx = 0.0 + lnhaty = 0.0 + e1y = 0.0 + e1z = 0.0 + lnhatz = 1.0 + e1x = 1.0 + lscorr = 0.0 + approx = lalParams['approx_name'] @@ -324,29 +334,87 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa fCut = XLALSimIMRPhenomXUtilsMftoHz(pWF['fRING']+8 * pWF['fDAMP'], pWF['Mtot']) deltaT_coarse = .5 * lalParams['coarse_fac'] / fCut + fS = fmin + fE = fCut - + PNEvolveOrbit_operands = [fRef, fmin, deltaT_coarse, m1_SI, m2_SI, fS, fE, s1x, s1y, s1z, s2x, s2y, s2z, lnhatx, lnhaty, lnhatz, e1x, e1y, e1z, lambda1, lambda2, quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx] + V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z = jax.lax.cond(True, fRef_equal_to_fmin, fRef_greater_than_fmin, operands = PNEvolveOrbit_operands) + if lalParams['coarse_fac'] > 1: + lenLow = len(V) + nbuffer = min(9, lenLow-1) + if (lenLow-1-nbuffer<0): + nbuffer = lenLow-1-nbuffer + vtrans = V[lenLow-1-nbuffer] + ftrans = pow(vtrans, 3)/piGM - FIX = jax.lax.select(True, fRef_equal_to_fmin, fRef_greater_than_fmin) + LNhatx_trans=LNhatx[lenLow-1-nbuffer] + LNhaty_trans=LNhaty[lenLow-1-nbuffer] + LNhatz_trans=LNhatz[lenLow-1-nbuffer] - if lalParams['coarse_fac'] > 1: - 'Do something' + E1x_trans = E1x[lenLow-1-nbuffer] + E1y_trans = E1y[lenLow-1-nbuffer] + E1z_trans = E1z[lenLow-1-nbuffer] + + S1x_trans = S1x[lenLow-1-nbuffer] + S1y_trans = S1y[lenLow-1-nbuffer] + S1z_trans = S1z[lenLow-1-nbuffer] + + S2x_trans = S2x[lenLow-1-nbuffer] + S2y_trans = S2y[lenLow-1-nbuffer] + S2z_trans = S2z[lenLow-1-nbuffer] + + fS=ftrans + fE=fCut + deltaT = 0.5/(fCut) + + + V_PN, Phi_PN, S1x_PN, S1y_PN, S1z_PN, S2x_PN, S2y_PN, S2z_PN, LNhatx_PN, LNhaty_PN, LNhatz_PN, E1x_PN, E1y_PN, E1z_PN, =XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT, m1_SI, m2_SI,fS,fE,S1x_trans,S1y_trans,S1z_trans,S2x_trans,S2y_trans,S2z_trans,LNhatx_trans,LNhaty_trans,LNhatz_trans,E1x_trans, E1y_trans, E1z_trans,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) + + lenPN=lenLow-nbuffer-1+len(V_PN) + + #if(lenPN < 4): + # XLALPrintError("Error in %s: no. of points is insufficient for spline interpolation",__func__) + # XLAL_ERROR(XLAL_EFUNC) + + + V_PN = V_PN[-(lenLow-nbuffer-1):lenPN] + LNhatx = LNhatx[-(lenLow-nbuffer-1):lenPN] + LNhaty_PN = LNhaty_PN[-(lenLow-nbuffer-1):lenPN] + LNhatz_PN = LNhatz_PN[-(lenLow-nbuffer-1):lenPN] + S1x_PN = S1x_PN[-(lenLow-nbuffer-1):lenPN] + S1y_PN = S1y_PN[-(lenLow-nbuffer-1):lenPN] + S1z_PN = S1z_PN[-(lenLow-nbuffer-1):lenPN] + + S2x_PN = S2x_PN[-(lenLow-nbuffer-1):lenPN] + S2y_PN = S2y_PN[-(lenLow-nbuffer-1):lenPN] + S2z_PN = S2z_PN[-(lenLow-nbuffer-1):lenPN] else: - 'Do something else' + copyLength=len(V)-1 + #if(copyLength < 4) { + #XLALPrintError("Error in %s: no. of points is insufficient for spline interpolation",__func__); + #XLAL_ERROR(XLAL_EFUNC); + ## Just create these arrays.. + + + ## copy coarse-grid data to fine-grid + ## destroy coarse-grid + ## + + fminPN=jnp.power(V_PN[0],3.)/piGM + if (fminPN<0.) | (fminPN>fmin): + return "Failure" - "Done!" PhenomXPInspiralArrays = None return PhenomXPInspiralArrays, 0 - -def fRef_equal_to_fmin(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx): +def fRef_equal_to_fmin(fRef, fmin, deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx): V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) return V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z @@ -357,14 +425,47 @@ def fRef_greater_than_fmin(fRef, fmin, deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, m1_SI, m2_SI,fS,fE,s1x,s1y,s1z,s2x,s2y,s2z,lnhatx,lnhaty,lnhatz,e1x,e1y,e1z,lambda1,lambda2,quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) if len(V['data']) > 1: - V2, Phi2, S1x2, S1y2, S1z2, S2x2, S2y2, S2z2, LNhatx2, LNhaty2, LNhatz2, E1x2, E1y2, E1z2, = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, + V2, Phi2, S1x2, S1y2, S1z2, S2x2, S2y2, S2z2, LNhatx2, LNhaty2, LNhatz2, E1x2, E1y2, E1z2 = XLALSimInspiralSpinTaylorPNEvolveOrbit(deltaT_coarse, m1_SI, m2_SI, fS, fE, s1x, s1y, s1z, s2x, s2y, s2z, lnhatx, lnhaty, lnhatz, e1x, e1y, e1z, lambda1,lambda2, quadparam1, quadparam2, spinO, tideO, phaseO, lscorr, approx) - ### append all ### + V = jnp.append(V, V2) + Phi = jnp.append(Phi, Phi2) + S1x = jnp.append(S1x, S1x2) + S1y = jnp.append(S1y, S1y2) + S1z = jnp.append(S1z, S1z2) + + S2x = jnp.append(S2x, S2x2) + S2y = jnp.append(S2y, S2y2) + S2z = jnp.append(S2z, S2z2) + + LNhatx = jnp.append(LNhatx, LNhatx2) + LNhaty = jnp.appnd(LNhaty, LNhaty2) + LNhatz = jnp.append(LNhatz, LNhatz2) + + E1x = jnp.append(E1x, E1x2) + E1y = jnp.append(E1y, E1y2) + E1z = jnp.append(E1z, E1z2) else: - ### destroy all - pass + # This means the generation failed. + V = jnp.array([0]) + Phi = jnp.array([0]) + S1x = jnp.array([0]) + S1y = jnp.array([0]) + S1z = jnp.array([0]) + + S2x = jnp.array([0]) + S2y = jnp.array([0]) + S2z = jnp.array([0]) + + LNhatx =jnp.array([0]) + LNhaty = jnp.array([0]) + LNhatz = jnp.array([0]) + + E1x = jnp.array([0]) + E1y = jnp.array([0]) + E1z = jnp.array([0]) + return V, Phi, S1x, S1y, S1z, S2x, S2y, S2z, LNhatx, LNhaty, LNhatz, E1x, E1y, E1z From c5f14b147f7aa9aa789199fc07400a77e07ab1b0 Mon Sep 17 00:00:00 2001 From: Harsh Narola Date: Tue, 7 Oct 2025 17:40:03 +0200 Subject: [PATCH 019/474] Add IMRPhenomX_InspiralAngles_SpinTaylor function --- src/ripplegw/waveforms/PrecessionStruct.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/src/ripplegw/waveforms/PrecessionStruct.py b/src/ripplegw/waveforms/PrecessionStruct.py index 003ea203..2d272b56 100644 --- a/src/ripplegw/waveforms/PrecessionStruct.py +++ b/src/ripplegw/waveforms/PrecessionStruct.py @@ -282,12 +282,11 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa chi2x: float, chi2y: float, chi2z: float, fmin: float, PrecVersion: int, pWF: dict, lalParams: dict): - - fRef = pWF['fRef'] m1_SI = pWF['m1_SI'] m2_SI = pWF['m2_SI'] + s1x=chi1x s1y=chi1y s1z=chi1z @@ -296,8 +295,6 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa s2y=chi2y s2z=chi2z - - piGM = jnp.pi * (pWF['m1_SI'] + pWF['m2_SI']) * (G / C) / (C * C) @@ -404,7 +401,6 @@ def IMRPhenomX_InspiralAngles_SpinTaylor(chi1x: float, chi1y: float, chi1z: floa ## copy coarse-grid data to fine-grid ## destroy coarse-grid - ## fminPN=jnp.power(V_PN[0],3.)/piGM if (fminPN<0.) | (fminPN>fmin): From f37a3ed7d2c515ff97341969330654f7b1f8d922 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Wed, 22 Oct 2025 11:16:40 +0200 Subject: [PATCH 020/474] Added NRTidalv3 to XAS (not fully correct yet) --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 206 ++++++++ src/ripplegw/waveforms/NRTidalv3_utils.py | 489 ++++++++++++++++++ 2 files changed, 695 insertions(+) create mode 100644 src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py create mode 100644 src/ripplegw/waveforms/NRTidalv3_utils.py diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py new file mode 100644 index 00000000..c87e677b --- /dev/null +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -0,0 +1,206 @@ +"""by Robin Chan""" + +import jax +import jax.numpy as jnp +from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C +from ..typing import Array +from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas +from .IMRPhenom_tidal_utils import get_quadparam_octparam, get_kappa +from .IMRPhenomD_NRTidalv2 import get_tidal_amplitude, get_spin_phase_correction, get_planck_taper # Same between v2 and v3 +from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper +from ripplegw.waveforms import IMRPhenomX_utils +from .IMRPhenomXAS import Amp, Phase + + +# This could be a general utils function +def _gen_IMRPhenomXAS_NRTidalv3( + f: Array, + theta_intrinsic: Array, + theta_extrinsic: Array, + bbh_amp: Array, + bbh_psi: Array, + no_taper: bool = False, +): + """ + Master internal function to get the GW strain for given parameters. The function takes + a BBH strain, computed from an underlying BBH approximant, e.g. IMRPhenomD, and applies the + tidal corrections to it afterwards + + Args: + f (Array): Frequencies in Hz + theta_intrinsic (Array): Internal parameters of the system: m1, m2, chi1, chi2, lambda1, lambda2 + theta_extrinsic (Array): Extrinsic parameters of the system: d_L, tc and phi_c + h0_bbh (Array): The BBH strain of the underlying model (i.e. before applying tidal corrections). + + Returns: + Array: Final complex-valued strain of GW. + """ + + m1, m2, _, _, lambda1, lambda2 = theta_intrinsic + M_s = (m1 + m2) * gt + Xa = m1 / (m1 + m2) + x = PI * f * M_s + x_23 = x**(2.0/3.0) + + # Compute kappa + kappa = get_kappa(theta=theta_intrinsic) + + # Compute amplitudes + A_T = get_tidal_amplitude(x_23, theta_intrinsic, kappa, distance=theta_extrinsic[0]) + f_merger = _get_merger_frequency(theta_intrinsic) + + # Decide whether to include the Planck taper or not + if no_taper: + A_P = jnp.ones_like(f) + AA_P = jnp.ones_like(f) + else: + A_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) + AA_P = get_planck_taper(f, f_merger) + + # Get tidal phase and spin corrections for BNS + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) + + # Check for local minimum TODO + fHzmrgcheck = 0.9 * f_merger + increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) + valid = (f >= fHzmrgcheck) & increasing + + + psi_T = NRTidalv3_phase * (1 - A_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * A_P + psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) + + # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) + h0 = AA_P * (bbh_amp + A_T) * jnp.exp(1.0j * -(bbh_psi + psi_T + psi_SS)) + + return h0 + + +def gen_IMRPhenomXAS_NRTidalv3( + f: Array, + params: Array, + f_ref: float, + use_lambda_tildes: bool = True, + no_taper: bool = False, +) -> Array: + """ + Generate NRTidalv3 frequency domain waveform following NRTidalv3 paper. + vars array contains both intrinsic and extrinsic variables + theta = [Mchirp, eta, chi1, chi2, D, tc, phic] + Mchirp: Chirp mass of the system [solar masses] + eta: Symmetric mass ratio [between 0.0 and 0.25] + chi1: Dimensionless aligned spin of the primary object [between -1 and 1] + chi2: Dimensionless aligned spin of the secondary object [between -1 and 1] + lambda1: Dimensionless tidal deformability of primary object + lambda2: Dimensionless tidal deformability of secondary object + D: Luminosity distance to source [Mpc] + tc: Time of coalesence. This only appears as an overall linear in f contribution to the phase + phic: Phase of coalesence + + f_ref: Reference frequency for the waveform + + Returns: + -------- + h0 (array): Strain + """ + + # Get component masses + m1, m2 = Mc_eta_to_ms(jnp.array([params[0], params[1]])) + if use_lambda_tildes: + lambda1, lambda2 = lambda_tildes_to_lambdas( + jnp.array([params[4], params[5], m1, m2]) + ) + else: + lambda1, lambda2 = params[4], params[5] + chi1, chi2 = params[2], params[3] + + theta_intrinsic = jnp.array([m1, m2, chi1, chi2, lambda1, lambda2]) + theta_extrinsic = params[6:] + phase_coeffs = IMRPhenomX_utils.PhenomX_phase_coeff_table + amp_coeffs = IMRPhenomX_utils.PhenomX_amp_coeff_table + + # Generate the BBH part: + bbh_theta_intrinsic = jnp.array([m1, m2, chi1, chi2]) + m1_s = m1 * gt + m2_s = m2 * gt + + M_s = m1_s + m2_s + eta = m1_s * m2_s / (M_s**2.0) + delta = jnp.sqrt(1.0 - 4.0 * eta) + mm1 = 0.5 * (1.0 + delta) + mm2 = 0.5 * (1.0 - delta) + + StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) + chia = chi1 - chi2 + + fM_s = f * M_s + fMs_RD, fMs_damp, _, _ = IMRPhenomX_utils.get_cutoff_fMs(m1, m2, chi1, chi2) + Psi = Phase(f, bbh_theta_intrinsic, phase_coeffs) + + # Generate the linear in f and constant contribution to the phase in order + # to roll the waveform such that the peak is at the input tc and phic + lina, linb, psi4tostrain = IMRPhenomX_utils.calc_phaseatpeak( + eta, StotR, chia, delta + ) + dphi22Ref = ( + jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, bbh_theta_intrinsic, phase_coeffs) / M_s + ) + linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + phifRef = ( + -(Phase(f_ref, bbh_theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + + PI / 4.0 + + PI + ) + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib + + A = Amp(f, bbh_theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) + + bbh_amp = A + bbh_psi = Psi + + # Use BBH waveform and add tidal corrections + return _gen_IMRPhenomXAS_NRTidalv3( + f, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper + ) + + +def gen_IMRPhenomXAS_NRTidalv3_hphc( + f: Array, + params: Array, + f_ref: float, + use_lambda_tildes: bool = True, + no_taper: bool = False, +): + """ + vars array contains both intrinsic and extrinsic variables + + IMRphenom denotes the name of the underlying BBH approximant used, before applying tidal corrections. + + theta = [Mchirp, eta, chi1, chi2, lambda1, lambda2, D, tc, phic, inclination] + Mchirp: Chirp mass of the system [solar masses] + eta: Symmetric mass ratio [between 0.0 and 0.25] + chi1: Dimensionless aligned spin of the primary object [between -1 and 1] + chi2: Dimensionless aligned spin of the secondary object [between -1 and 1] + D: Luminosity distance to source [Mpc] + tc: Time of coalesence. This only appears as an overall linear in f contribution to the phase + phic: Phase of coalesence + inclination: Inclination angle of the binary [between 0 and PI] + + f_ref: Reference frequency for the waveform + + Returns: + -------- + hp (array): Strain of the plus polarization + hc (array): Strain of the cross polarization + """ + iota = params[-1] + h0 = gen_IMRPhenomXAS_NRTidalv3( + f, params[:-1], f_ref, use_lambda_tildes=use_lambda_tildes, no_taper=no_taper + ) + + hp = h0 * (1 / 2 * (1 + jnp.cos(iota) ** 2)) + hc = -1j * h0 * jnp.cos(iota) + + return hp, hc diff --git a/src/ripplegw/waveforms/NRTidalv3_utils.py b/src/ripplegw/waveforms/NRTidalv3_utils.py new file mode 100644 index 00000000..35f5e5f7 --- /dev/null +++ b/src/ripplegw/waveforms/NRTidalv3_utils.py @@ -0,0 +1,489 @@ +"""by Robin Chan""" + +import jax +import jax.numpy as jnp +from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C +from ..typing import Array +from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas +from .IMRPhenom_tidal_utils import get_quadparam_octparam, get_kappa +from .IMRPhenomD_NRTidalv2 import get_tidal_amplitude + + +""" +Corrections from NRTidalv3: (2311.07456) +--------------------------- + +- Phase +- Amplitude -> same as NRTidalv2, reuse +- Spin effects +- Spin precession effects + +Uses merger frequency as stopping point -> tapering +|_> For (much) later: look into RLO frequency +""" + + +""" +Compute the merger frequency of a BNS system for NRTidalv3 (https://arxiv.org/pdf/2311.07456.pdf). +This uses a new fit from Gonzalez, et. al (2022) Eq. (23) of https://arxiv.org/abs/2210.16366. +""" +def _get_merger_frequency(theta: Array): + """ + Computes the merger frequency in Hz of the given system. This is defined in equation (41) in 2311.07456 and the lal source code. + Pretty much literally copied from LAL at the moment (XLALSimNRTunedTidesMergerFrequency_v3) + + Args: + theta (Array): Intrinsic parameters with order (m1, m2, chi1, chi2, lambda1, lambda2) + kappa (float, optional): Tidal parameter kappa. Defaults to None, so that it is computed from the given parameters theta. + + Returns: + float: The merger frequency in Hz. + """ + m1, m2, chi1, chi2, lambda1, lambda2 = theta + M = m1 + m2 + m1_s = m1 * gt + m2_s = m2 * gt + q = m1_s / m2_s + + Xa = q/(1.0+q) + Xb = 1.0 - Xa + + nu = Xa*Xb + + Xa_2 = Xa*Xa + Xa_3 = Xa_2*Xa + Xb_2 = Xb*Xb + Xb_3 = Xb_2*Xb + + kappa2eff = 3.0*nu*(Xa_3*lambda1 + Xb_3*lambda2) + + a_0 = 0.22 + + a_1M = 0.80 + a_1S = 0.25 + b_1S = -1.99 # This was not written in 2311.07456, but can be found in Table 2 of 2210.16366. + + a_1T = 0.0485 + a_2T = 0.00000586 + a_3T = 0.10 + a_4T = 0.000186 + + b_1T = 1.80 + b_2T = 599.99 + b_3T = 7.80 + b_4T = 84.76 + + Xval = 1.0 - 4.0*nu + + p_1S = a_1S*(1.0 + b_1S*Xval) + p_1T = a_1T*(1.0 + b_1T*Xval) + p_2T = a_2T*(1.0 + b_2T*Xval) + p_3T = a_3T*(1.0 + b_3T*Xval) + p_4T = a_4T*(1.0 + b_4T*Xval) + + kappa2eff2 = kappa2eff*kappa2eff + + Sval = Xa_2*chi1 + Xb_2*chi2 + + QM = 1.0 + a_1M*Xval + QS = 1.0 + p_1S*Sval + + num = 1.0 + p_1T*kappa2eff + p_2T*kappa2eff2 + den = 1.0 + p_3T*kappa2eff + p_4T*kappa2eff2 + QT = num / den + + # dimensionless angular frequency of merger + Qfit = a_0*QM*QS*QT + + Momega_merger = nu*Qfit*TWO_PI + + + # convert from angular frequency to frequency (divide by 2*pi) and then convert from dimensionless frequency to Hz (divide by mtot * LAL_MTSUN_SI) + fHz_merger = Momega_merger / TWO_PI / (M * MRSUN / C) + + return fHz_merger + + +##################################################################################################################################### +################################################### TIDAL PHASE CORRECTIONS ################################################### +##################################################################################################################################### + + +def general_planck_taper(x, y1, y2): + return jnp.where( + x < y1, + 0, + jnp.where( + x > y2, + 1, + 1.0 / (jnp.exp((y2 - y1) / (x - y1) + (y2 - y1) / (x - y2)) + 1.0), + ), + ) + + +""" +Tidal phase correction for NRTidalv3, Eq. (27,30), from Abac, et. al. (2023) (https://arxiv.org/pdf/2311.07456.pdf) +and is a function of x = angular_orb_freq^(2./3.) +""" +def get_tidal_phase( + M_omega: Array, # Dimensionless angular GW frequency + NRTidalv3_coeffs: Array, # NRTidalv3 coefficients + PN_coeffs: Array # 7.5 PN coefficients to be used as constraints +): + """ + SimNRTunedTidesFDTidalPhase_v3 + """ + + s1 = NRTidalv3_coeffs[0] + s2 = NRTidalv3_coeffs[1] + + exps2s3 = NRTidalv3_coeffs[3] + + s2Mom = -s2*M_omega*2.0 + exps2Mom = jnp.cosh(s2Mom) + jnp.sinh(s2Mom) + + # expression for the effective love number enhancement factor, see Eq. (27) of https://arxiv.org/pdf/2311.07456.pdf.*/ + dynk2bar = 1.0 + ((s1) - 1)*(1.0/(1.0 + exps2Mom*exps2s3)) - ((s1-1.0)/(1.0 + exps2s3)) - 2.0*M_omega*((s1) - 1)*s2*exps2s3/((1.0 + exps2s3)*(1.0 + exps2s3)) + + PN_x = M_omega**(2.0/3.0) # redundant computation? + PN_x_2 = PN_x * PN_x + PN_x_3 = PN_x * PN_x_2 + PN_x_3over2 = PN_x * jnp.sqrt(PN_x) + PN_x_5over2 = PN_x_3over2 * PN_x + + kappaA = NRTidalv3_coeffs[4] + kappaB = NRTidalv3_coeffs[5] + + dynkappaA = kappaA*dynk2bar + dynkappaB = kappaB*dynk2bar + + # Pade Coefficients + n_5over2A = NRTidalv3_coeffs[6] + n_3A = NRTidalv3_coeffs[7] + d_1A = NRTidalv3_coeffs[8] + + n_5over2B = NRTidalv3_coeffs[9] + n_3B = NRTidalv3_coeffs[10] + d_1B = NRTidalv3_coeffs[11] + + # 7.5PN Coefficients + c_NewtA = PN_coeffs[0] + c_NewtB = PN_coeffs[5] + + # Pade Coefficients constrained with PN + n_1A = NRTidalv3_coeffs[12] + n_3over2A = NRTidalv3_coeffs[13] + n_2A = NRTidalv3_coeffs[14] + d_3over2A = NRTidalv3_coeffs[15] + + n_1B = NRTidalv3_coeffs[16] + n_3over2B = NRTidalv3_coeffs[17] + n_2B = NRTidalv3_coeffs[18] + d_3over2B = NRTidalv3_coeffs[19] + + factorA = -c_NewtA*PN_x_5over2*dynkappaA + factorB = -c_NewtB*PN_x_5over2*dynkappaB + + # Pade approximant, see Eq. (32) of https://arxiv.org/pdf/2311.07456.pdf. */ + numA = 1.0 + (n_1A*PN_x) + (n_3over2A*PN_x_3over2) + (n_2A*PN_x_2) + (n_5over2A*PN_x_5over2) + (n_3A*PN_x_3) + denA = 1.0 + (d_1A*PN_x) + (d_3over2A*PN_x_3over2) # + (d_2A*PN_x_2) + + numB = 1.0 + (n_1B*PN_x) + (n_3over2B*PN_x_3over2) + (n_2B*PN_x_2) + (n_5over2B*PN_x_5over2) + (n_3B*PN_x_3) + denB = 1.0 + (d_1B*PN_x) + (d_3over2B*PN_x_3over2) # + (d_2B*PN_x_2) + + ratioA = numA/denA + ratioB = numB/denB + + tidal_phaseA = factorA*ratioA + tidal_phaseB = factorB*ratioB + + tidal_phase = tidal_phaseA + tidal_phaseB + + return tidal_phase + + +def get_tidalphasePN_coeffs(theta_intrinsic: Array): + """ + Coefficients or the PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, see Eq. (45) of https://arxiv.org/pdf/2311.07456.pdf + XLALSimNRTunedTidesSetFDTidalPhase_PN_Coeffs + """ + + # PN_coeffs = jnp.zeros(10) + + m1, m2, _, _, _, _ = theta_intrinsic + M = m1 + m2 + Xa = m1 / M + Xb = m2 / M + + # Powers of Xa and Xb + Xa_2 = Xa*Xa + Xa_3 = Xa_2*Xa + Xa_4 = Xa_3*Xa + Xa_5 = Xa_4*Xa + + Xb_2 = Xb*Xb + Xb_3 = Xb_2*Xb + Xb_4 = Xb_3*Xb + Xb_5 = Xb_4*Xb + + den_a = 11.0*Xa-12.0 + den_b = 11.0*Xb-12.0 + + # 7.5PN Coefficients + PN_coeffs0 = -3.0*den_a/(16*Xa*Xb_2) #(3.0)*(12.0 -11.0*Xa)/(16*Xa*Xb_2) + PN_coeffs1 = (-1300.0*Xa_3 + 11430.0*Xa_2 + 4595.0*Xa -15895.0)/(672.0*den_a) #-5.0*(260.0*Xa_3 - 2286.0*Xa_2 - 919.0*Xa + 3179.0)/(672.0*(11.0*Xa-12.0)) + PN_coeffs2 = -PI + PN_coeffs3 = (22861440.0*Xa_5 - 102135600.0*Xa_4 + 791891100.0*Xa_3 + 874828080.0*Xa_2 + 216234195.0*Xa -1939869350.0)/(27433728.0*den_a)# (5.0*(4572288.0*Xa_5 - 20427120.0*Xa_4 + 158378220.0*Xa_3 +174965616.0*Xa_2 + 43246839.0*Xa -387973870.0))/(27433728.0*(11.0*Xa - 12.0)) + PN_coeffs4 = -PI*(10520.0*Xa_3 -7598.0*Xa_2 +22415.0*Xa - 27719.0)/(672.0*den_a) + + PN_coeffs5 = -3.0*den_b/(16*Xb*Xa_2) #(3.0)*(12.0 -11.0*Xb)/(16*Xb*Xa_2) + PN_coeffs6 = (-1300.0*Xb_3 + 11430.0*Xb_2 + 4595.0*Xb -15895.0)/(672.0*den_b) #-5.0*(260.0*Xb_3 - 2286.0*Xb_2 - 919.0*Xb + 3179.0)/(672.0*(11.0*Xb-12.0)) + PN_coeffs7 = -PI + PN_coeffs8 = (22861440.0*Xb_5 - 102135600.0*Xb_4 + 791891100.0*Xb_3 + 874828080.0*Xb_2 + 216234195.0*Xb -1939869350.0)/(27433728.0*den_b) #(5.0*(4572288.0*Xb_5 - 20427120.0*Xb_4 + 158378220.0*Xb_3 +174965616.0*Xb_2 + 43246839.0*Xb -387973870.0))/(27433728.0*(11.0*Xb - 12.0)) + PN_coeffs9 = -PI*(10520.0*Xb_3 -7598.0*Xb_2 +22415.0*Xb - 27719.0)/(672.0*den_b) + + + PN_coeffs = jnp.array([PN_coeffs0, PN_coeffs1, PN_coeffs2, PN_coeffs3, PN_coeffs4, + PN_coeffs5, PN_coeffs6, PN_coeffs7, PN_coeffs8, PN_coeffs9]) + + return PN_coeffs + + +def get_NRTidalv3_coefficients( + theta_intrinsic: Array, + PN_coeffs, # 7.5 PN coefficients to be used for constraints +): + """ + Set the NRTidalv3 effective love number and phase coefficients in an array for use here and in the IMRPhenomX*_NRTidalv3 implementation + XLALSimNRTunedTidesSetFDTidalPhase_v3_Coeffs + """ + + NRTidalv3_coeffs = jnp.zeros(20) + + m1, m2, _, _, lambda1, lambda2 = theta_intrinsic + M = m1 + m2 + Xa = m1 / M + Xb = m2 / M + q = Xa/Xb + + # Coefficients for the effective enhancement factor: + s10 = 1.273000423 #s10 + s11 = 3.64169971e-03 #s11 + s12 = 1.76144380e-03 #s12 + + s20 = 2.78793291e+01 #s20 + s21 = 1.18175396e-02 #s21 + s22 = -5.39996790e-03 #s22 + + s30 = 1.42449682e-01 #s30 + s31 = -1.70505852e-05 #s31 + s32 = 3.38040594e-05 #s32 + + kappa2T = get_kappa(theta_intrinsic) # WHY DO THE PAPERS HAVE A PREFACTOR OF 2/13 BUT CODE (BOTH RIPPLE AND LAL) 3/13???? + + NRTidalv3_coeffs = NRTidalv3_coeffs.at[0].set(s10 + s11*kappa2T + s12*q*kappa2T) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[1].set(s20 + s21*kappa2T + s22*q*kappa2T) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[2].set(s30 + s31*kappa2T + s32*q*kappa2T) + + s2s3 = NRTidalv3_coeffs[1]*NRTidalv3_coeffs[2] + NRTidalv3_coeffs = NRTidalv3_coeffs.at[3].set(jnp.cosh(s2s3) + jnp.sinh(s2s3)) + + NRTidalv3_coeffs = NRTidalv3_coeffs.at[4].set(3.0*Xb*Xa*Xa*Xa*Xa*lambda1) # kappaA + NRTidalv3_coeffs = NRTidalv3_coeffs.at[5].set(3.0*Xa*Xb*Xb*Xb*Xb*lambda2) # kappaB + + # Exponent parameters: + alpha = -8.08155404e-03 #alpha + beta = -1.13695919e+00 #beta + + kappaA_alpha = jnp.pow(NRTidalv3_coeffs[4] + 1, alpha) #kappaA_alpha + kappaB_alpha = jnp.pow(NRTidalv3_coeffs[5] + 1, alpha) #kappaB_alpha + + Xa_beta = jnp.pow(Xa, beta) #Xa_beta + Xb_beta = jnp.pow(Xb, beta) #Xb_beta + + # Pade approximant coefficients: + n_5over20 = -9.40654388e+02 #n_5over20 + n_5over21 = 6.26517157e+02 #n_5over21 + n_5over22 = 5.53629706e+02 #n_5over22 + n_5over23 = 8.84823087e+01 #n_5over23 + + n_30 = 4.05483848e+02 #n_30 + n_31 = -4.25525054e+02 #n_31 + n_32 = -1.92004957e+02 #n_32 + n_33 = -5.10967553e+01 #n_33 + + d_10 = 3.80343306e+00 #d_10 + d_11 = -2.52026996e+01 #d_11 + d_12 = -3.08054443e+00 #d_12 + + NRTidalv3_coeffs = NRTidalv3_coeffs.at[6].set(n_5over20 + n_5over21*Xa + n_5over22*kappaA_alpha + n_5over23*Xa_beta) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[7].set(n_30 + n_31*Xa + n_32*kappaA_alpha + n_33*Xa_beta) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[8].set(d_10 + d_11*Xa + d_12*Xa_beta) + + NRTidalv3_coeffs = NRTidalv3_coeffs.at[9].set(n_5over20 + n_5over21*Xb + n_5over22*kappaB_alpha + n_5over23*Xb_beta) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[10].set(n_30 + n_31*Xb + n_32*kappaB_alpha + n_33*Xb_beta) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[11].set(d_10 + d_11*Xb + d_12*Xb_beta) + + # 7.5PN Coefficients + c_1A, c_3over2A, c_2A, c_5over2A = PN_coeffs[1:5] + c_1B, c_3over2B, c_2B, c_5over2B = PN_coeffs[6:10] + + inv_c1_A = 1.0 / c_1A + NRTidalv3_coeffs = NRTidalv3_coeffs.at[12].set(c_1A + NRTidalv3_coeffs[8]) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[13].set(((c_1A*c_3over2A) - c_5over2A - (c_3over2A)*NRTidalv3_coeffs[8] + NRTidalv3_coeffs[6]) * inv_c1_A) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[14].set(c_2A + c_1A*NRTidalv3_coeffs[8]) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[15].set(-(c_5over2A + c_3over2A*NRTidalv3_coeffs[8] - NRTidalv3_coeffs[6]) * inv_c1_A) + + inv_c1_B = 1.0 / c_1B + NRTidalv3_coeffs = NRTidalv3_coeffs.at[16].set(c_1B + NRTidalv3_coeffs[11]) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[17].set(((c_1B*c_3over2B) - c_5over2B - (c_3over2B)*NRTidalv3_coeffs[11] + NRTidalv3_coeffs[9]) * inv_c1_B) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[18].set(c_2B + c_1B*NRTidalv3_coeffs[11]) + NRTidalv3_coeffs = NRTidalv3_coeffs.at[19].set(-(c_5over2B + c_3over2B*NRTidalv3_coeffs[11] - NRTidalv3_coeffs[9]) * inv_c1_B) + + return NRTidalv3_coeffs + + +""" +PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, +see Eq. (22) and (45) of https://arxiv.org/pdf/2311.07456.pdf +and is a function of x = angular_orb_freq^(2./3.) +""" +def get_tidal_phase_PN( + M_omega, # Dimensionless angular GW frequency + Xa, #< Mass of companion 1 divided by total mass + lambda1, #< dimensionless tidal deformability of companion 1 + lambda2, #< dimensionless tidal deformability of companion 2 + PN_coeffs #< 7.5 PN coefficients +): + """ + SimNRTunedTidesFDTidalPhase_PN + """ + Xb = 1.0 - Xa + + PN_x = M_omega **(2.0/3.0) # pow(M_omega, 2.0/3.0) + PN_x_2 = PN_x * PN_x # pow(PN_x, 2) + PN_x_3over2 = PN_x * jnp.sqrt(PN_x) # pow(PN_x, 3.0/2.0) + PN_x_5over2 = PN_x_3over2 * PN_x # pow(PN_x, 5.0/2.0) + + kappaA = 3.0*Xb*Xa*Xa*Xa*Xa*lambda1 + kappaB = 3.0*Xa*Xb*Xb*Xb*Xb*lambda2 + + # 7.5PN Coefficients + c_NewtA = PN_coeffs[0] + c_1A = PN_coeffs[1] + c_3over2A = PN_coeffs[2] + c_2A = PN_coeffs[3] + c_5over2A = PN_coeffs[4] + + c_NewtB = PN_coeffs[5] + c_1B = PN_coeffs[6] + c_3over2B = PN_coeffs[7] + c_2B = PN_coeffs[8] + c_5over2B = PN_coeffs[9] + + factorA = -c_NewtA*PN_x_5over2*kappaA + factorB = -c_NewtB*PN_x_5over2*kappaB + + tidal_phasePNA = factorA*(1.0 + (c_1A*PN_x) + (c_3over2A*PN_x_3over2) + (c_2A*PN_x_2) + (c_5over2A*PN_x_5over2)) + tidal_phasePNB = factorB*(1.0 + (c_1B*PN_x) + (c_3over2B*PN_x_3over2) + (c_2B*PN_x_2) + (c_5over2B*PN_x_5over2)) + + tidal_phasePN = tidal_phasePNA + tidal_phasePNB + + return tidal_phasePN + + +###################################################################################################################################### +######################################################### SPIN EFFECTS ######################################################### +###################################################################################################################################### + + +def get_spin_phase_correction_v3(x: Array, theta: Array) -> Array: + """ + Get the higher order spin corrections with updated unversal relation, as detailed in Section III A of 2311.07456 + + Args: + x (Array): Angular frequency, in particular, x = (pi M f)^(2/3) + theta (Array): Intrinsic parameters (mass1, mass2, chi1, chi2, lambda1, lambda2) + + Returns: + Array: Higher order spin corrections to the phase + """ + + # Compute auxiliary quantities + m1, m2, chi1, chi2, lambda1, lambda2 = theta + m1_s = m1 * gt + m2_s = m2 * gt + M_s = m1_s + m2_s + eta = m1_s * m2_s / (M_s**2.0) + + # Compute the auxiliary variables + X1 = m1_s / M_s + X1sq = X1 * X1 + chi1_sq = chi1 * chi1 + + X2 = m2_s / M_s + X2sq = X2 * X2 + chi2_sq = chi2 * chi2 + + # Compute quadrupole parameters + quadparam1, octparam1 = get_quadparam_octparam(lambda1) + quadparam2, octparam2 = get_quadparam_octparam(lambda2) + + # Remove 1 for the BBH baseline, from here on, quadparam is "quadparam hat" as referred to in the NRTidalv2 paper etc + quadparam1 -= 1 + quadparam2 -= 1 + octparam1 -= 1 + octparam2 -= 1 + + # Get phase contributions + SS_2 = -50.0 * quadparam1 * chi1_sq * X1sq - 50.0 * quadparam2 * chi2_sq * X2sq + + SS_3 = ( + 5.0 + / 84.0 + * (9407.0 + 8218.0 * X1 - 2016.0 * X1sq) + * quadparam1 + * X1sq + * chi1_sq + + 5.0 + / 84.0 + * (9407.0 + 8218.0 * X2 - 2016.0 * X2sq) + * quadparam2 + * X2sq + * chi2_sq + ) + + SS_3p5 = ( + -400.0 * PI * quadparam1 * chi1_sq * X1sq + - 400.0 * PI * quadparam2 * chi2_sq * X2sq + ) + SSS_3p5 = ( + 10.0 + * ((X1sq + 308.0 / 3.0 * X1) * chi1 + (X2sq - 89.0 / 3.0 * X2) * chi2) + * quadparam1 + * X1sq + * chi1_sq + + 10.0 + * ((X2sq + 308.0 / 3.0 * X2) * chi2 + (X1sq - 89.0 / 3.0 * X1) * chi1) + * quadparam2 + * X2sq + * chi2_sq + - 440.0 * octparam1 * X1 * X1sq * chi1_sq * chi1 + - 440.0 * octparam2 * X2 * X2sq * chi2_sq * chi2 + ) + + prefac = 3.0 / (128.0 * eta) + psi_SS = prefac * ( + SS_2 * x ** (-1.0 / 2.0) + SS_3 * x ** (1.0 / 2.0) + (SS_3p5 + SSS_3p5) * x + ) + + return psi_SS + + +####################################################################################################################################### +#################################################### SPIN PRECESSION EFFECTS #################################################### +####################################################################################################################################### + + +# TODO, better to implement straight into XP(HM)? + From 73b8145498eaaef0d042260059b907b1dc782cf1 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Wed, 29 Oct 2025 14:23:11 +0100 Subject: [PATCH 021/474] Very quick XAS test looks good --- test/XAS_testing.ipynb | 341 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 341 insertions(+) create mode 100644 test/XAS_testing.ipynb diff --git a/test/XAS_testing.ipynb b/test/XAS_testing.ipynb new file mode 100644 index 00000000..1cb838e3 --- /dev/null +++ b/test/XAS_testing.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "id": "4a34eed3", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from ripplegw.constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C\n", + "from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc\n", + "from ripplegw import ms_to_Mc_eta, lambdas_to_lambda_tildes, get_eff_pads, get_match_arr\n", + "\n", + "import lalsimulation as lalsim\n", + "import lal\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "672ec381", + "metadata": {}, + "outputs": [], + "source": [ + "# Frequency grid\n", + "T = 128\n", + "f_l = 20.0\n", + "f_sampling = 2 * 4096\n", + "f_u = f_sampling // 2\n", + "f_ref = f_l\n", + "\n", + "delta_t = 1 / f_sampling\n", + "tlen = int(round(T / delta_t))\n", + "freqs = np.fft.rfftfreq(tlen, delta_t)\n", + "df = freqs[1] - freqs[0]\n", + "fs = freqs[(freqs > f_l) & (freqs < f_u)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c190d1fb", + "metadata": {}, + "outputs": [], + "source": [ + "m1 = 2 # In solar masses\n", + "m2 = 2\n", + "chi1 = 0.141 # Dimensionless spin\n", + "chi2 = 0.141\n", + "tc = 0.0 # Time of coalescence in seconds\n", + "phic = 0.0 # Time of coalescence\n", + "dist_mpc = 440 # Distance to source in Mpc\n", + "inclination = 0.0 # Inclination Angle\n", + "\n", + "q = m1/m2\n", + "Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", + "\n", + "theta_ripple = jnp.array(\n", + " [\n", + " Mc,\n", + " eta,\n", + " chi1,\n", + " chi2,\n", + " dist_mpc,\n", + " tc,\n", + " phic,\n", + " inclination,\n", + " ]\n", + ")\n", + "fs_ripple = jnp.arange(f_l, f_u, df)[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "166999f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate ripple plus strain\n", + "hp_ripple, _ = gen_IMRPhenomXAS_hphc(fs_ripple, theta_ripple, f_ref)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6b134673", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up LAL parameters\n", + "\n", + "laldict = lal.CreateDict()\n", + "\n", + "IMRphenom = \"IMRPhenomXAS\"\n", + "approximant = lalsim.SimInspiralGetApproximantFromString(IMRphenom)\n", + "\n", + "theta = np.array(\n", + " [m1, m2, chi1, chi2, dist_mpc, tc, phic, inclination]\n", + ")\n", + "\n", + "m1_kg = theta[0] * lal.MSUN_SI\n", + "m2_kg = theta[1] * lal.MSUN_SI\n", + "distance = dist_mpc * 1e6 * lal.PC_SI\n", + "\n", + "# Generate LAL plus strain\n", + "hp, _ = lalsim.SimInspiralChooseFDWaveform(\n", + " m1_kg,\n", + " m2_kg,\n", + " 0.0,\n", + " 0.0,\n", + " chi1,\n", + " 0.0,\n", + " 0.0,\n", + " chi2,\n", + " distance,\n", + " inclination,\n", + " phic,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " df,\n", + " f_l,\n", + " f_u,\n", + " f_ref,\n", + " laldict,\n", + " approximant,\n", + ")\n", + "\n", + "freqs_lal = np.arange(len(hp.data.data)) * df\n", + "\n", + "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", + "hp_lalsuite = hp.data.data[mask_lal]\n", + "hp_LAL = hp.data.data[mask_lal]\n", + "f = freqs_lal[mask_lal]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d06ad0d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = 0.75\n", + "plt.subplots(1, 1, figsize=(17, 7))\n", + "# plt.subplot(1, 2, 1)\n", + "\n", + "plt.plot(f, hp_LAL.real, \"-\", label=\"LAL\", alpha=a)\n", + "plt.plot(f, hp_ripple.real, \"-\", label=\"ripple\", alpha=a)\n", + "plt.title(\n", + " r\"$h_+$ IMRPhenomXAS ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(m1,m2,chi1,chi2)\n", + ")\n", + "plt.xlabel(\"Frequency\")\n", + "plt.ylabel(\"Strain\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "00da7dc7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Get the merger frequency to check the Planck taper window\n", + "\n", + "merger_params = jnp.array([m1, m2, chi1, chi2])\n", + "\n", + "## Get amplitude and angle for comparison\n", + "\n", + "A_lalsuite = jnp.abs(hp_lalsuite)\n", + "angle_lalsuite = np.unwrap(np.angle(hp_lalsuite))\n", + "phase_lalsuite = hp_lalsuite / A_lalsuite\n", + "\n", + "A_ripple = jnp.abs(hp_ripple)\n", + "angle_ripple = np.unwrap(np.angle(hp_ripple))\n", + "phase_ripple = hp_ripple / A_ripple\n", + "\n", + "# Choose whether we plot the angle or the phase\n", + "plot_angle = True\n", + "\n", + "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", + "plt.subplot(2, 1, 1)\n", + "\n", + "# Plot the amplitude\n", + "plt.plot(freqs_lal[mask_lal], A_lalsuite, \"-\", label=\"LAL\")\n", + "plt.plot(fs_ripple, A_ripple, \"--\", label=\"ripple\")\n", + "\n", + "plt.title(\n", + " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.ylim(1e-27, 1e-21)\n", + "plt.xlim(f_l - 5)\n", + "plt.legend()\n", + "\n", + "# Plot the angle or the phase\n", + "plt.subplot(2, 1, 2)\n", + "if plot_angle:\n", + " plt.plot(freqs_lal[mask_lal], angle_lalsuite, \"-\", label=\"LAL\")\n", + " plt.plot(fs_ripple, angle_ripple, \"--\", label=\"ripple\")\n", + " name = \"Angle\"\n", + "else:\n", + " plt.plot(freqs_lal[mask_lal], phase_lalsuite, \"-\", label=\"LAL\")\n", + " plt.plot(fs_ripple, phase_ripple, \"--\", label=\"ripple\")\n", + " name = \"Phase\"\n", + "plt.legend()\n", + "plt.title(\n", + " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9e7c07f6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ecLgyfGkFyvE587sBwL8Q6AEEjMzMTH377be6++671bt370L3R0ZGas6cOVqwYIH69u3rtn7j4+MLjPpPnjxZkooNu0WpUqWKJBUK702bNlVqaqq2bNli/wCdlJRUaOX64OBgdevWTfPmzdOBAwfspxJs375dv/zyS4H97rrrLn3++efatm2bfXQw3/Hjx+3nbVqtVp0+fVrh4eH2++vUqaPIyMgip5/ms1qt2r59u7p161bovs2bN+vyyy8v6ekotfypyOvWrSsQOKxWqxISEgpNaz9x4oSSkpIKBe3t27crNzfXbesgeKL/3bt369SpU2rWrFmB7Rs2bND999/v8HHuOJfa0fMsnT3Wvn37avXq1Zo/f36R08OdVZq2Sjr+kurP98EHH0g6e4qEu0Zgy9qPo9dcMvd17927t9566y1Nnz5dTz/9tKSzpwvMnDlTsbGxBdbROHPmjA4cOKBatWq5fNWD1q1bF/rbJ52dpp6enq53331XTZs2dalNZwTK8TnzuwHAvxDoAQSMBQsWKD09vdD5yfmuvPJK1a5dW7Nnz3ZroE9MTNStt96q7t27a/Xq1frss8903333FTkSW5z80bbnn39e99xzjypWrKhevXrpnnvu0TPPPKM77rhDw4cP15kzZzR16lS1aNGi0LXgX375Zf3888+69tpr9fjjjysvL0+TJ09Wq1at7AuySdL//vc/LV26VLGxsRoyZIhatmypU6dOacOGDVq8eLF9Wm56eroaNGig3r17Kzo6WlWrVtXixYu1du1avf322w6PZffu3crKyioUlDIzM7Vnz54yL4xWnCZNmqh169ZavHhxgUu+5dd0/uuyefNmSSq0Pf/5cjXQWywWderUScuWLSuw3d39Z2Zmql+/fnr22WcLfOGyfv16nTp1SrfddpvDGt1xLrWj51mSnnrqKS1YsEC9evXSqVOn9NlnnxW4/9wp5Y6er9K0JTl3/CXVL0l79+7Vc889J+n/1n0ojZKOz5V+HL3mkvmve2xsrO6++249++yzOnbsmJo1a6aPP/5Y+/bt00cffVSgjTVr1qhLly4aPXp0gXUHpkyZopSUFPtU8++//16HDh2SdHax0fDwcNWqVcs+M+Jc+SPW59/nTJv5inutAuH4nP3dAOBnTFxhHwDcqlevXkZoaKiRkZHhcJ8BAwYYFStWNE6cOGG/7Nvx48cL7NO/f3+jSpUqhR57/mWD8h+fkJBg9O7d26hWrZpRo0YNY+jQoUZmZmaBxzpz2TrDMIxXXnnFqF+/vhEUFFTg/l9//dVo3bq1UalSJePiiy82PvvssyIvW2cYhvH7778b7du3NypVqmQ0adLEmDZtWpH7JicnG3FxcUZUVJRRsWJFIyIiwrjhhhuM6dOn2/fJzs42Ro4caURHRxvVqlUzqlSpYkRHRxvvvfeew+fYMAzjq6++MiQZ27ZtK7B9zZo1hiTjhx9+KPSYnj17GuHh4UZ4eLhRsWJFo3Llyvbb48aNK7a/802YMMGoWrVqgcvvOappwoQJhiQjJSWlwPb//ve/RlhYmGGz2Qq1/8gjjxijR48utD09Pd2QZNxzzz2F7nNn/zk5OUbPnj2N++67r9D9zzzzjNGwYcMiH+duRT3PhnH2d0XFXBItX3HPl6tt5XPl+B3Vb7PZjE6dOhk1atQwBg4caFStWrXY9krzfnC1n+Jec8Pwjdc9MzPTePrpp42IiAgjJCTEiImJMX7++edCj8+/fN75z1mjRo1cvoxePkeXdXO2TWfei/58fIbh3fcIAO8h0ANAKTn6QgBl179//1JfL9owDCMlJcWoWbOm8eGHH7qvqHM4CnALFy40LBaLsWXLFo/0axiGYbVajb59+xq33HKLkZubW+C+rKwsIyIiwpg4caLH+j9XWZ9ndz9frh6/o/qnTJliSDI++eQTY86cOYYkY+/evQ7bKe37wdl+invNDcP/Xndf5I3fXTN5+z0CwHvcc2FdAAB8SHh4uP773/9q/Pjx9lX93SEvL09ZWVmyWq0F/p1v6dKluueee9SmTRu39Xm+Rx55RElJSfr6669VoULBM+dmzpypihUr6tFHH/VY/+cq6/Ps7ufL1eMvqv59+/Zp1KhR6tWrlx544AF7beef3iKV7f3gSj/FvealOe6y8tTvl5m88btrJm+/RwB4j8UwnLgGEwCgkDFjxujll1/W8ePHXV74CMUry2XrPCn/NT/XzJkzvVbn/v371bhxY4WGhha4pNhPP/2ka6+91is1BDLDMNS1a1dt3LhRf//9t+rVq6e8vDzVqFFDkZGReuqpp3T//ffbF7As7fvBlX54zQEAxSHQA0ApEeiBwPL+++/r0Ucf1SeffKIHHnjAvn3WrFl68cUXdfz4caWnp6tixYp+0Q8AIPAR6AEAAAAA8EOcQw8AAAAAgB8i0AMAAAAA4IcKL5WKAmw2m44cOaJq1arJYrGYXQ4AAAAAIMAZhqH09HRFRkYqKMjxODyBvgRHjhxRVFSU2WUAAAAAAMqZgwcPqkGDBg7vJ9CXoFq1apLOPpFhYWEmVwMAAAAACHRpaWmKioqy51FHCPQlyJ9mHxYWRqAHAAAAAHhNSad9sygeAAAAAAB+iEAPAAAAAIAfItA7EB8fr5YtWyomJsbsUgAAAAAAKMRiGIZhdhG+LC0tTeHh4UpNTeUcegAAAACAxzmbQxmhBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAAAAAA/RKAHAAAAAMAPEegBAAAAAPBDBHoAAAAAAPwQgR4AAAAA4LcMwzC7BNMQ6AEAAAAAPunQoUOyWCzF/gQFBRXatmHDBrNL9woCvQPx8fFq2bKlYmJizC4FAAAAAALesWPHCgXzqKioUrXVvn17exsff/yxmyv1HRajPM9PcEJaWprCw8OVmpqqsLAws8sBAAAAgICxatUqXX311V7p66OPPtKgQYO80ldZOZtDCfQlINADAAAAgPtkZ2crNDTUtP4zMjJ0wQUXmNa/M5zNoUy5BwAAAAB43GuvvSaLxeJymO/du7dOnTolm80mwzAc/mRmZurzzz8vsb0qVaqU9hB8DiP0JWCEHgAAAABKxzAMBQW5Po6cm5urChUqlKnv4mYC+HoMZoQeAAAAAGCKEydO2Fegd9aGDRvso+1lDfOSFBISYm8vUBHoAQAAAABusWXLFlksFtWuXdup/ZOSkuyh+/LLL/dYXYZhKDk5Wa+//rpsNpvH+vE2Aj0AAAAAoEyefPJJWSwWRUdHl7hv37597SE+IiLCC9WdVadOHT377LOyWCxe69PTyj6PAQAAAABQLt1999365ptvnNr377//VsuWLT1cUflCoAcAAAAAuMSVUe5APofdbEy5BwAAAAA4ZdKkSU6HeavVSpj3MAI9AAAAAKBYY8aMkcVi0RNPPFHsfhEREfbz40tzuTq4hmcYAAAAAFCk559/XhaLRS+//HKx+23dulWGYSgpKclLlUHiHHoAAAAAwDny8vLUt29fffvttyXuu2XLFrVp08YLVaEojNA7EB8fr5YtWyomJsbsUgAAAADAK15++WVVrFixxDC/b98+GYZBmDeZxWCVgmKlpaUpPDxcqampCgsLM7scAAAAAHC7qVOn6vHHHy9xv/3796thw4ZeqKh8czaHMuUeAAAAAMqhvLw83Xnnnfr+++9L3Dc9PV1Vq1b1QlVwBVPuAQAAAKAcSUtLU/fu3VWxYsUSw3x6eroMwyDM+yhG6AEAAACgHMjKylLlypWd2vfUqVOqUaOGhytCWTFCDwAAAAAB7J9//pHFYnEqzJ88eVKGYRDm/QSBHgAAAAAC0H333SeLxaKmTZuWuK/NZpNhGKpZs6YXKoO7MOUeAAAAAAKE1WpVhQrOxbzLLrtMmzdv9nBF8CRG6AEAAADAz40cOVIWi8WpMH/DDTfIZrMR5gMAI/QAAAAA4GdSUlL03XffadCgQU4/Zv78+brlllsUFMS4bqAg0AMAAACAHzAMQ7///ru6dOni0uNOnjzJufEBikAPAAAAAD7q8OHDSk9P16WXXuryYw3D8EBF8CXMtQAAAAAAH2EYhgzDsK9Q36BBA5fC/KZNm+xtIPAR6AEAAADARLm5ufrkk09ksVgUFBSkoKAgzZkzx+nH33bbbfYQHx0d7cFK4WuYcg8AAAAAXmKz2ZSQkKA2bdqUua39+/erYcOGbqgK/opADwAAAABuZrPZZLVatW7dOl111VVua/fYsWOqXbu229qDfyPQAwAAAPBZhmHIYrEUuG2z2RQcHCxJysnJUXp6un0V93Pvy9/farU6dX32ovq22WwyDEMVKlTQP//8ox07dqhmzZpavXq1fv/9d82fP7+MR1i8Rx99VG+99ZaqVKni0X7gnwj0AAAAALwmPT1dYWFhZpfhsx5//HG9+eabBHg4hUAPAAAAwGsI8wWtXLlSV155ZYFZBYCzWOUeAAAAADzommuuUUJCgrKzs5WUlGSfxm8Yhq6++mrCPEqNQO9AfHy8WrZsqZiYGLNLAQAAAODD5s6dq7vuukvLli2zB/Vzf1asWKFLL71UlSpVUkRERIE1AYCysBiGYZhdhC9LS0tTeHi4UlNTmR4EAAAAlNG9996rL774Qu+++66GDRsmSV4JuDabTUFBQQVuS7JvO3fxvfMX4gO8zdkcyjn0AAAAAEzhzdB8bpgv6va5tRDm4S+Ycg8AAAAAgB8i0AMAAAAA4IcI9AAAAAAA+CECPQAAAAAAfohADwAAAACAHyLQAwAAAADghwj0AAAAAAD4IQI9AAAAAAB+iEAPAAAAAIAfItADAAAAAOCHCPQAAAAAAPghAj0AAAAAAH6IQA8AAAAAgB8i0AMAAAAA4IcI9AAAAAAA+CECPQAAAAAAfohADwAAAACAHyLQAwAAAADghwj0AAAAAAD4IQI9AAAAAAB+iEAPAAAAAIAfItADAAAAAOCHCPQAAAAAAPghAj0AAAAAAH6IQA8AAAAAgB+qYHYB3tC4cWOFhYUpKChINWrU0NKlS80uCQAAAACAMikXgV6SVq1apapVq5pdBgAAAFCuGYZhdglAwGDKPQAAAACvs1gsZpcA+D3TA/3y5cvVq1cvRUZGymKxaN68eYX2iY+PV+PGjRUaGqrY2FitWbPGpT4sFos6deqkmJgYzZ49202VAwAAAABgHtOn3GdkZCg6OlqDBg3SnXfeWej+L7/8UiNGjNC0adMUGxuriRMnqlu3btq5c6fq1KkjSWrbtq3y8vIKPfbXX39VZGSkVq5cqfr16yspKUldu3ZVmzZtdNlll3n82AAAAAAA8BTTA32PHj3Uo0cPh/dPmDBBQ4YM0cCBAyVJ06ZN08KFCzVjxgyNGjVKkrRp06Zi+6hfv74kqV69err55pu1YcMGh4E+Oztb2dnZ9ttpaWmuHA4AAAAAAF5h+pT74uTk5Gj9+vXq2rWrfVtQUJC6du2q1atXO9VGRkaG0tPTJUmnT5/Wb7/9platWjncf9y4cQoPD7f/REVFle0gAAAAAADwAJ8O9CdOnJDValXdunULbK9bt66OHj3qVBvJycm65pprFB0drSuvvFIPPvigYmJiHO7/7LPPKjU11f5z8ODBMh0DAAAAAACeYPqUe09r0qSJNm/e7PT+ISEhCgkJ8WBFAAAAAACUnU+P0NeqVUvBwcFKTk4usD05OVkREREmVQUAAAAAgPl8OtBXqlRJ7du315IlS+zbbDablixZoo4dO5pYGQAAAAAA5jJ9yv3p06e1Z88e++3ExERt2rRJNWvWVMOGDTVixAj1799fV1xxhTp06KCJEycqIyPDvuq9p8THxys+Pl5Wq9Wj/QAAAAAAUBqmB/p169apS5cu9tsjRoyQJPXv31+zZs1S3759dfz4cb300ks6evSo2rZtq59//rnQQnnuFhcXp7i4OKWlpSk8PNyjfQEAAAAA4CrTA33nzp1lGEax+wwdOlRDhw71UkUAAAAAAPg+nz6HHgAAAAAAFI1ADwAAAACAHyLQAwAAAADghwj0DsTHx6tly5aKiYkxuxQAAAAAAAoh0DsQFxenhIQErV271uxSAAAAAAAohEAPAAAAAIAfItADAAAAAOCHCPQAAAAAAPghAj0AAAAAAH6IQO8Aq9wDAAAAAHwZgd4BVrkHAAAAAPgyAj0AAAAAAH6IQA8AAAAAgB8i0AMAAAAA4IcI9AAAAAAA+CECPQAAAAAAfohADwAAAACAHyLQO8B16AEAAAAAvoxA7wDXoQcAAADczzAMs0sAAgaBHgAAAIDXWSwWs0sA/B6BHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAAAAAA/RKAHAAAAAMAPEegdiI+PV8uWLRUTE2N2KQAAAAAAFEKgdyAuLk4JCQlau3at2aUAAAAAAFAIgR4AAAAAAD9EoAcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIHegfj4eLVs2VIxMTFmlwIAAAAAQCEEegfi4uKUkJCgtWvXml0KAAAAAACFEOgBAAAAAPBDBHoAAAAAAPwQgR4AAAAAAD9EoAcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAAAgNcYhmF2CUDAINADAAAA8DqLxWJ2CYDfK1WgT0lJ0Ycffqhnn31Wp06dkiRt2LBBhw8fdmtxAAAAAACgaBVcfcCWLVvUtWtXhYeHa9++fRoyZIhq1qypb7/9VgcOHNAnn3ziiTq9Lj4+XvHx8bJarWaXAgAAAABAIS6P0I8YMUIDBgzQ7t27FRoaat9+8803a/ny5W4tzkxxcXFKSEjQ2rVrzS4FAAAAAIBCXA70a9eu1SOPPFJoe/369XX06FG3FAUAAAAAAIrncqAPCQlRWlpaoe27du1S7dq13VIUAAAAAAAonsuB/tZbb9XYsWOVm5sr6ezqlAcOHNAzzzyju+66y+0FAgAAAACAwlwO9G+//bZOnz6tOnXqKDMzU506dVKzZs1UrVo1vfbaa56oEQAAAAAAnMflVe7Dw8O1aNEirVy5Ulu2bNHp06fVrl07de3a1RP1AQAAAACAIrgc6PNdc801uuaaa9xZCwAAAAAAcJJTgX7SpElONzh8+PBSFwMAAAAAAJzjVKB/5513Ctw+fvy4zpw5o+rVq0uSUlJSdMEFF6hOnToEegAAAAAAvMCpRfESExPtP6+99pratm2r7du369SpUzp16pS2b9+udu3a6ZVXXvF0vQAAAAAAQKVY5f7FF1/U5MmTdfHFF9u3XXzxxXrnnXf0wgsvuLU4AAAAAABQNJcDfVJSkvLy8gptt1qtSk5OdktRAAAAAACgeC4H+htuuEGPPPKINmzYYN+2fv16PfbYY1y6DgAAAECxDh48aHYJQMBwOdDPmDFDERERuuKKKxQSEqKQkBB16NBBdevW1YcffuiJGgEAAAAEgM2bN+vPP/80uwwgYLh8HfratWvrxx9/1K5du7Rjxw5J0iWXXKIWLVq4vTgAAAAAgeO+++6z/zshIcHESoDAYDEMwzC7CF+Wlpam8PBwpaamKiwszOxyAAAAAL/zzjvvaP78+fr9998LbCeKAEVzNoe6PEI/aNCgYu+fMWOGq00CAAAACGAjRowwuwQgILkc6P/9998Ct3Nzc7Vt2zalpKTo+uuvd1thAAAAAPyXYRh6+OGHWWcL8CCXA/13331XaJvNZtNjjz2mpk2buqUoXxAfH6/4+HhZrVazSwEAAAD8xpw5c3TgwAG1aNGCMA94mNvOod+5c6c6d+6spKQkdzTnMziHHgAAAHCexWJxel/OoQeK5mwOdfmydY7s3btXeXl57moOAAAAgJ+ZNGmS2SUA5YrLU+7PX9DCMAwlJSVp4cKF6t+/v9sKAwAAAOA/Nm/erCeeeMLsMoByxeVAv3HjxgK3g4KCVLt2bb399tslroAPAAAAIPAcPHhQbdu2NbsMoNxxOdAvXbrUE3UAAAAA8FMNGzY0uwSgXHL5HPrrr79eKSkphbanpaVx2ToAAACgnNm/f7/ZJQDllsuBftmyZcrJySm0PSsrSytWrHBLUQAAAAB8V3Z2tsaNG6euXbuqcePGZpcDlFtOT7nfsmWL/d8JCQk6evSo/bbVatXPP/+s+vXru7c6AAAAAD7j999/V3Z2trp162Z2KQDkQqBv27atLBaLLBZLkVPrK1eurMmTJ7u1OAAAAAC+4fjx4+rcubPZZQA4h9OBPjExUYZhqEmTJlqzZo1q165tv69SpUqqU6eOgoODPVIkAAAAAHMlJyebXQKA8zgd6Bs1aiRJstlsHisGAAAAgG85efKkHnzwQXXo0MGt7UZERLi1PaA8cirQL1iwQD169FDFihW1YMGCYve99dZb3VIYAAAAAPPExcXp5MmTqlGjhn788Uf9+OOPbm1/06ZNbm0PKI8shmEYJe0UFBSko0ePqk6dOgoKcrwwvsVikdVqdWuBZktLS1N4eLhSU1MVFhZmdjkAAACAxxmGUeznfnf1AaBozuZQp0boz51mz5R7AAAAILARtgH/4Nmv3QAAAAD4HU8G+ri4OGVnZ3usfaA8cWqEftKkSU43OHz48FIXAwAAAMBce/fuVbNmzTzW/pQpUzzWNlDeOBXo33nnHacas1gsBHoAAADAz2RnZ+ubb75Rv379PNrPgw8+6NH2gfLGqUXxyjMWxQMAAECgSkxM1KxZs7R582bNnz/f4/0RPQDnuHVRPEfyfyEtFktZmgEAAABggquuukpHjx71Sl8VKpQpegAoQqkWxfvoo4/UunVrhYaGKjQ0VK1bt9aHH37o7toAAAAAeJC3wrwkrVq1ymt9AeWFy1+TvfTSS5owYYKGDRumjh07SpJWr16t//znPzpw4IDGjh3r9iIBAAAA+K8nnnhCMTExZpcBBByXA/3UqVP1wQcf6N5777Vvu/XWW3XZZZdp2LBhBHoAAAAABdStW9fsEoCA5HKgz83N1RVXXFFoe/v27ZWXl+eWogAAAAB4RmZmpi644AKv9BUbG6tLLrlETz75pFf6A8obl8+hf+CBBzR16tRC26dPn67777/fLUUBAAAAcK/k5GRZLBavhfmJEyfqjz/+0KxZs1S5cmWv9AmUNy5ftm7YsGH65JNPFBUVpSuvvFKS9Ndff+nAgQN68MEHVbFiRfu+EyZMcG+1JuCydQAAAPBXBw8e1Lx58/Tdd99p6dKlXu2bS9QBpeexy9Zt27ZN7dq1kyTt3btXklSrVi3VqlVL27Zts+/HpewAAAAAc/z444/q2bOn2WUA8DCXA723v9kDAAAA4LwjR46YHuYXL15sav9AeeFyoPdHiYmJGjRokJKTkxUcHKw///xTVapUMbssAAAAwO0OHjxoWt95eXkKDg42rX+gvHE50GdlZWny5MlaunSpjh07JpvNVuD+DRs2uK04dxkwYIBeffVVXXvttTp16pRCQkLMLgkAAADwCLPOXeececD7XA70gwcP1q+//qrevXurQ4cOPn+u/N9//62KFSvq2muvlSTVrFnT5IoAAAAAzyFYA+WHy4H+hx9+0I8//qirr77aLQUsX75c48eP1/r165WUlKTvvvtOt99+e4F94uPjNX78eB09elTR0dGaPHmyOnTo4FT7u3fvVtWqVdWrVy8dPnxYvXv31nPPPeeW2gEAAABfkpubq6uuusrr/f7+++9e7xNAKa5DX79+fVWrVs1tBWRkZCg6Olrx8fFF3v/ll19qxIgRGj16tDZs2KDo6Gh169ZNx44ds+/Ttm1btW7dutDPkSNHlJeXpxUrVui9997T6tWrtWjRIi1atMht9QMAAABmGzNmjCwWiypVquTVftetWyfDMHTdddd5tV8AZ7l8HfqffvpJkyZN0rRp09SoUSP3FmOxFBqhj42NVUxMjKZMmSJJstlsioqK0rBhwzRq1KgS21y9erXGjBmjX375RZI0fvx4SdLIkSOL3D87O1vZ2dn222lpaYqKiuI69AAAAPApKSkpqlGjhqk1ML0f8Axnr0Pv8gj9FVdcoaysLDVp0kTVqlVTzZo1C/y4U05OjtavX6+uXbv+X8FBQeratatWr17tVBsxMTE6duyY/v33X9lsNi1fvlyXXnqpw/3HjRun8PBw+09UVFSZjwMAAABwl59++kkWi8X0MA/AfC6fQ3/vvffq8OHDev3111W3bl2PLop34sQJWa1W1a1bt8D2unXraseOHU61UaFCBb3++uu67rrrZBiGbrrpJt1yyy0O93/22Wc1YsQI++38EXoAAADALJmZmbrttttUpUoVzZs3z+xyJEmzZ882uwSg3HM50K9atUqrV69WdHS0J+rxiB49eqhHjx5O7RsSEsJl7QAAAOBTLr/8cu3cudPsMuyYag/4Bpen3F9yySXKzMz0RC2F1KpVS8HBwUpOTi6wPTk5WREREV6pAQAAADCbL4V5AL7D5UD/v//9T0899ZSWLVumkydPKi0trcCPO1WqVEnt27fXkiVL7NtsNpuWLFmijh07urUvAAAAAAD8ictT7rt37y5JuuGGGwpsNwxDFotFVqvVpfZOnz6tPXv22G8nJiZq06ZNqlmzpho2bKgRI0aof//+uuKKK9ShQwdNnDhRGRkZGjhwoKuluyQ+Pl7x8fEuHw8AAAAQyM6fPQvAPC5ftu733393eN/WrVs1dOhQlwpYtmyZunTpUmh7//79NWvWLEnSlClTNH78eB09elRt27bVpEmTFBsb61I/peXs5QIAAAAAT/HkQtSuuPnmm7Vw4UKzywACnrM51OVAf7709HTNmTNHH374odavXx9wI9oEegAAAJhl3bp16t+/vxISEswuRZJ0yy236Pvvvze7DCDgOZtDXZ5yn2/58uX66KOPNHfuXEVGRurOO+9UfHx8aZsDAAAAoP/7IO+L7r//frNLAHAOlwL90aNHNWvWLH300UdKS0tTnz59lJ2drXnz5qlly5aeqhEAAAAIeIZhqHnz5tq7d6/ZpRTy0EMPaciQIYqJiTG7FADncHqV+169euniiy/Wli1bNHHiRB05ckSTJ0/2ZG0AAABAQMvKylJGRoaGDx+uRo0a+WSYP378uD744AN16NDBZ87lB3CW0yP0P/30k4YPH67HHntMzZs392RNPoFV7gEAAOAp//zzj9asWaOBAwcqKyvL7HIcCgsLU61atcwuA4ADTo/Qr1y5Uunp6Wrfvr1iY2M1ZcoUnThxwpO1mSouLk4JCQlau3at2aUAAAAgQOzevVu9evVS06ZNde+99/p0mJfOngYAwHc5HeivvPJKffDBB0pKStIjjzyiL774QpGRkbLZbFq0aJHS09M9WScAAADg9+644w798MMPZpcBIECU6bJ1O3fu1EcffaRPP/1UKSkpuvHGG7VgwQJ31mc6LlsHAAAAd6lSpYrOnDljdhlOq1q1KgN3gAmczaFOj9AX5eKLL9abb76pQ4cOac6cOWVpCgAAAAAAuKBMgT5fcHCwbr/99oAbnQcAAAAAwFe5JdADAAAA5U15uBrSuHHjzC4BQDEI9A7Ex8erZcuWiomJMbsUAAAA+Jh+/fopMjJSqampTu1vGIYsFovfnD8fGRmpHTt2aOjQoWaXAqAYZVoUrzxgUTwAAACcz2KxSJKmT5+uIUOGONzParWqcePGOnTokLdKK5MpU6boscceU1AQ436AmZzNoRW8WBMAAAAQ8AzD0KJFi9StWzezS3Hali1b1KZNG7PLAOAiAj0AAADgBhkZGZLOXurNXwwbNkyTJk0yuwwApcRcGgAAAKCUDMNQRkaGrr76alWtWtWvwjwA/0egBwAAAErpkUceUdWqVbVq1SqzSymV/LUAAPgnAj0AAADggqeeesrsEgBAEoHeIS5bB3jO3r17lZycbHYZAAA45dixY/rqq6/UqlUr/fe//9WECRPMLgkAJLEonkNxcXGKi4uzXy4AKI5hGPrtt9/UunVr1a1b1+xy3O7LL79UZmamBgwYUOa2jh07pmbNmkk6+7wBgJnOnDmjMWPG6I477lDHjh3NLgc+aOPGjWrXrp39dkJCgonVAEBBjNADbjB//nx17dpVjRo1cnvbu3fv1qBBg7Rr1y63t+2M3Nxc3XPPPRo4cKCOHz9e5vb+/vvvMrfx+++/68EHH9SJEyfK3Nb5bDYbXzQA5cirr76q8ePH66qrrjK7FPioMWPGmF0CADjECD3gBj/99JMkKTs72y3t7dmzR3PmzNGwYcN0/fXX69ChQ1q0aJEOHjzolvZdkZeXZ//36dOnVbt2ba/XcL7OnTvb//3JJ5+4rd2cnBy1adNGF198sRYsWOC2dgH4ru3bt5e5DcMw9O+//6pmzZpuqAgAAOcxQg844fTp0/r888/VrVs3rwS96OhovfTSS3r88cd16NAhSbL/10y+NnKdmJjo1vZWrlypXbt26fvvv3druwAC26OPPqoLL7xQP/74o9mlAC574oknzC4BQBkQ6GFnGIbuu+8+Pf/882aX4hNycnK0bNkyzZo1S927d9f999+vX3/9VbfddpvH+z5z5owkacWKFR7vy9tKujzOgQMHNH36dJ0+fdpLFQGA86xWq/1vdL7p06dLkl566SUzSgJK7cCBA2rSpInZZQAoAwI97P766y/NmTNHr7/+utmlmC4vL09169ZVly5dNHDgQP3xxx9ml2Qab16f9rnnnlOjRo30yCOPKC4uzu3tjxkzRg899JDDmQZci9d9kpKStG7dOrPLKDf+/vtvzZgxw+dm0XiTYRjKysryeD/R0dGqUqWKUlNTPd4X4Al9+/aVYRgyDENRUVFmlwOgjAj05VRSUpKaNm2qcePG2bd544OQvzh48KBSUlLc2ubOnTs1duxYv/4QOGLECG3cuNFj7Z/7fpw3b57b23/55Zf10UcfafPmzW5vG/8nLy9PkZGRiomJKddfhnlT69atNXjwYM2ZM8fsUrzu33//ldVqVb9+/VS5cmXt2bPHo/3lL+z5+++/e7Qf+I4tW7aYXYJbffHFF2aXAMCNCPTl1CuvvKJ//vlHzz33nMf7+uqrr7R06VKP92OmokbFDMPQ5s2bderUKUnSpZdeqtGjR+s///mPt8srk3NHrefPn1/g0j2lcezYMaf2K+1IY05OjmbNmqUDBw443MfR4oVlGaG32Wylfqwn2vGmM2fOaPTo0fYve879MmzGjBkmVWWenTt3ymKx6L777vN63+VtVsTOnTtVs2ZNde7cWZ9//rkkafLkySZXhUCQm5urvn37ymKxaN++fWaXAwAOEegdiI+PV8uWLRUTE2N2KR5x7srlnrRnzx717dtX119/vVf68yWXXXaZ2rZtqwsvvFDS/wXU1atXO92GGdNnT58+ra+//trt57CfPHlSLVq0UN++fd3WZlHPzxtvvKGBAwfq0ksvdVs/JZk6darCwsJcem3Pt2DBAlksFkVEROjIkSNlrmnu3LmaPXt2mdtx5MSJE3ryySdlsVhUpUoVjR071v5lz7lfjPjqFPDff/9dkyZN8kh9l1xyiSS5bbTcZrPp5MmTmjJliq677jqlpaW5pd1AkH+Vi5UrV5pcCQLFkSNHNHjwYFWqVElfffWV2eUAQIkI9A7ExcUpISFBa9euNbsUlxiGoR9//FG9e/fWzz//7PX+v/76a11zzTX2y6sdPnzY5TYMw9CLL76o+fPnu7W2+Ph43XjjjcrIyChxX1dHanNycuz/7t69uwzD0LZt21yu0WwzZsxQtWrV1KdPHz3wwANubXvChAnavXu30/s7E7SOHTsmi8Wiu+66y77t119/laRCi1Y5o7Qj9I8//rgyMjLUr18/lx63ZMkSLVq0SJLsiy0eP35c48ePL1UdkvTjjz/qrrvuUu/evdWvXz8lJSWVuq2iLFu2TBaLRbVr19a7775b5D6eCPRr167VDTfc4LZTPjp37qwnnnhCv/zyi1va86Tg4GDVqlVLw4YN04oVKzRhwgSzS/K6uXPnaubMmR5pe/ny5R5p15EdO3YoMzPTq30GujNnzhQ7K8sZjz76qCwWi+rXr18uZxYB8F8E+gDz6aefqmfPnpo7d6569Ojh8f4OHTqk2rVry2KxKCMjQ3369NEff/yhxx9/3KV2zp1mPH/+fL366qu6/fbby1SbzWbT2LFj7YFp6NChWrx4sd57770ytZsvOztbzzzzjDp06KCPP/7Yvv2XX37x+Pl2p06d0ogRI9x+LvjgwYPt/3b3Oey5ublubU+S/QuCb7/91ump/FLpgrthGGrSpIlCQ0PLvN7EbbfdJovFoq5du+qmm24q04hrZmZmgd+fnj176ttvv7XfLuul/QzD0PLly+2nKXTp0qXEx7gj0CcmJmrDhg265ZZb9Nlnnyk2Nla//fabOnXqVKr2HNm7d6/Lj3n66ad1xRVXyGq1urWWc/Xv31/XX399kadgOPOlpCt8dRZFQkKC3nnnHWVnZ6t3794aNGiQ2y7fuW/fPh06dEgZGRn206I86fDhw+rUqZNeffVVXXrpperQoYPH+ywP8v/uN2nSRI0aNdKOHTtcevzff/+tnTt3SpLef/99t9cHAN5QwewC4F5ffvmlU/u5azXvc1dHveOOO+z//vfff51uY8KECXrllVe0fPlytWnTplSj+vlSUlLUr18/vfjii9q/f79Gjx4tqeAHVndMJS/p+SvuPOiyPPfDhw/XnDlzdOLECUnSO++847MfxsvK0XE5WqzQlS8MnF3l/sCBA/rll19UvXp1Va1a1R6Op02bpieffNLp/lJSUlS9enX77QULFhS4Pz093em2pLPvrzVr1ujff//VzTffrNjYWP35558utVGSQ4cOKSIiQhUrVpQkhYWFOb2gY1kC/YcffqhRo0bp5MmT9m0LFy60/9vV58odDMPQyZMnVatWLWVkZOjtt9+WdHbWz/Dhw8vcfv6U+tq1a9u35U8l9+QilEePHlW9evUknV0w0tcuudaqVStJBf9mp6SkqEGDBmVq9+6779Y333wjSW45vcURm82mFStWaMqUKfb+8mcD+OMMLl+SlZWlypUrS5JGjx6t5ORkSWdnKOWf8uKIYRg6ePCgGjVqZN929OhRzxULAB7GCH2ACQoq/Uta3AfvrKwsvffee5o1a5bDfRISEpxq63xPPfWUUlJS7KP6ZQmoNWrU0MKFC3XllVeWaWSyrF94uOsLk/O/3Jg8ebI9zLvCMAy9//77WrVqlVvqks6OcLkyXdnV1zV/9POJJ57QLbfcYv+SpKTgXpbn/tzHWiwWNWrUSA8//LD69OlT4IP/uWHTkfT0dJ08eVLffvutatSooVGjRpW6rnPNnDlTwcHB6tixo26++WZJZy856Q4pKSl6/vnnZbFYFBUVVWABRFdmEbga6Pfs2WM/NWDIkCFOPb/esnbtWgUFBal27dr67bffCpzKUdaZOHv27NHEiRMVHBysOnXqaNmyZYX2cfciiWvWrLGvAp8f5iXZv/z0Re54f8+ZM0ft2rVTYmKiPVxLBU+Xcpf9+/fLYrEoODhYnTt3LtAfymb69Onq1q1bgdH0l19+2enHr1mzRkFBQQXCvCRt377dbTUCgLcR6AOMO6+jbRiG3n33XVksFlWuXFlxcXEaOHCg1qxZ43D/8/99bj0vvfSSw3NuXWWz2bwyTdITSgo47p5OK0mLFi3So48+qquvvtrlxxb1nrJYLLrooovUvXt3LV682B0lFpKVlaWnnnpKkyZN0sKFC+0f6j15rfiRI0c6tV9xr6HNZtPgwYMVFhamWrVq2U9jeOONN5yu4/z2Z82aZT8FYtCgQU6344zc3FxZrVZZLBbVqFFDr7/+uv2+rVu3lqpNZwJ9SkqKRo4cKYvFoubNmysyMrJUfXnauVP8XXkNi7Ns2TKFhYWpefPmBa56kT/y724ZGRl68803ZbFYFBsbq+bNm5fq7+eJEydksVhksVj0/fffl6kmb88suu+++7Rx40Y9+uijHu1n/fr1aty4cakem5GRodWrV7vlSxyr1erSmiX+4pFHHtGvv/5a4BKnJfnyyy/t79vY2FgPVgcA5iDQBxhnw05J+1ksFgUFBRU5rdiZke+iPqy98sorLk1TLk737t114YUXFnsOeWmCX15enjZu3FjmD5uu9p2Xl6fDhw9r5MiRqlq1apn6Lkr+OYKlUdI06xUrVjjVTnHP6d69e4u8xNe5i3/lX5nB0SyU/Ofcmec+f5/JkyfbP+jdcMMNTo8EFvXlVb5ff/21wIJKjk4RcMbLL7+sGjVqaODAgQVOaXGFo0sqPvjgg7JYLKpUqZIqVHDv2VeOAn1OTo6uvfZa+5cHb731llv7LQ2LxSKr1epwXQR3LF5mtVr10Ucf2d9rXbp08cqpA//73/9ksVhUtWpVPfPMMwXu27Vrl0tt5S+EmO/WW28tVU0nTpxQxYoVFRQUZJ9S726TJk1yuNCdu5/3X375RRMnTixTG8eOHdMzzzyjqKgoXXXVVW45l/uee+5RixYtPLaQoKft2bOn2NfKmRlDGRkZatWqle655x53lgYAPodAH2BKO3p5+PBht15a7s8//7RPBy6r48ePa8WKFQWCQf5Cd9OnT3e5vfOfI8Mw9M033+j777/XQw89pHbt2mns2LFlK9rJ/u+44w5ZLBZVrFhRDRo0KHXA8dRlrCwWi+rUqVPsPmX58iM/4DRr1szpS3w5CvSu1BEbGyuLxVLg/Offfvut2Mec335CQoLGjh1bIORIpZthcf578t1339Vnn32mMWPGlOkLgfPlXx89KChIn376qdvaPd/5gX7x4sUKCQlRSEiIWy4vdu7ijWX1ww8/qEKFCqpcubJHQvaECRNUoUIFPfTQQ25vuyifffaZ/ffq2WefLVNbo0aN0k033VTmy5zm5eVp/fr1stlsql27tr29hIQEt59SkM/diycW5bbbblP37t0LzLJwxSeffKL4+Hj1799fb775pn3tmY8++sildnJycgo9j/nT/N01q8QbTp8+rffff1+XXHKJmjdvroYNG7rcxrlfQM+ePbvAqYAAEKhYFC/AlCbQe2oK808//aQ+ffqUuZ1GjRopMzNTCxcudNuXBNLZoJEfbs5X1lGN4p7TtLQ0XXLJJWUaNT/XK6+8opdeeklz5swxZSTC2SDtrim2pVknIjs7W6GhoW7pX5Jef/31AlPTzxUcHOxye0VNtXbXZQOPHj2qmTNnqnnz5rr22mvd0mZJzn3/f/755/r888/d2v6MGTP0yiuvlGqa/vHjxwssIPjTTz/Z/71q1Sp169atTLWdf/7/U089Vab2nHHul0jHjx93W7v5YbC0l0BdvHix5s6dq5MnT+rrr7/WCy+8UGif0v7/Jzk52asLmRmGoWXLlrn1i+/+/fuXuY2srCzVq1dPjRo10qZNm8pelEmeeeYZvfnmmwW2lebLzLFjx+qOO+5Q27ZtPXoVCn/mjoU8AfgWRugDTEkfjnJzczV27FivXZ7F1RBnGIaGDRtmv/3555/bp7ye+8G7LF5++WV7kHdlNX5XnP865K/AK52dDeGuMC/JvjL1I488UqrHnzx5UmFhYaX+YF3Sa5x/v7uune2ozqKm3I8dO1YWi8WtYb4kpZm+7snriudf7subYd4Tp42cz5WR3by8PDVq1Mg+46S008UdsVqt9vUAatWq5da2HTEMQ+3bt/fK8+3KFSSSk5PVvHlzDRo0SDfeeKOmTZumr7/+WpIcfgnmir/++kvjx49XRESE2rZt6/EQu2vXLvv/L9wZ5ksrMzPTfglJ6exVEFJSUtx+CVNvstlshcJ8WUyaNMnlxzhzOU5/N27cOPvaSAACCyP0AcbR6OWiRYt00003ebmakp0bBleuXFlolefHHnus2McXF0KdCajTpk0rcR93iIiI8HgfxQXrkkJwWTgKVudeEsudSqr53JXHvb1yt2EYxQb6r776ym2r3RfFUwsU+iPDMJSWlqagoCCFhYV5pI+PPvrI5enR7vDWW2/5xPoD+bKzs/X4448XWDsifyV9dzj3b8z5py78+OOPbusnX3Z2tubPn6++ffu6ve3Smjx5stLS0vTCCy+oevXqOnXqlNtm1x05ckRPPvmkhg4dquuuu84tbTryzz//KCIiQhdccIFH+0FBgXp5WwBnEegdiI+PV3x8vN9N2Tr/f/B9+vSxj454WlH/w3D0gePff//VxRdfXGh66Ny5c516fHF95jt3VPzjjz8uth13u+yyy7zanyteeOEFxcXFqVq1am5p79zXYO3aterQoYNb2nVV/fr1Pdq+sx+Iipty78mA4MnV/32Ro+PdsmWLoqOj7beHDBnitj4Nw/Do2gP+5p9//tGNN96of/75x6n9XQkV586ectfsLGf44u/R+TW5c10N6eyXJD/99JO+/vprjwa/jRs3ql27dmrQoIEOHjzosX5wdhDBm6ekADAXU+4diIuLU0JCgtauXWt2KS45/xrl3grzrujdu7cuv/zyIs/1PH9RqvOvC+6Kc89LHjBgQJH7BNK31o6OZcuWLQVGzl577TXdeeedZV7oKl9GRoZ9ES6zwrwvcfeK8b5kyZIlZpdQpKysLPt78NwwL0kffPCB2/pZtGiRV86JP5cv/041bdrU6TDvDMMwtGTJElksFtWsWdNt7RbHnac/ucv69evVunVr++KvnubMlWtK69dff1WrVq30119/6bvvvpMkHTp0yGP94awHH3zQ7BIAeBGBPkAkJSXJYrEUmrLuCWUdwZg7d672799f5H3nh1Jnrme9efNmPffcc4qPjy9TXYEkJSVFjzzyiD3gbNy4scD9f/75p9tWl548ebJb2imJYRgyDMMrq1eXVmJiokfPs509e7bH2nZG165dTe3/XDabTXPnzpXFYlHlypXd3r5hGKYcb0JCgs+NEp97zrYnnD592n6euref87Iugugpf//9t9tOk1u4cKH9C6+kpCS3tFmcefPmacGCBZLOPr8JCQm68cYbPd4vAJRXgTuUVM4899xzZpdQpIEDB7q0//khs6QPtu+9957ee+89l+sKRPkfip3lqctFecpbb71lapj3hdkc/fr1M7sEn1GaS1o5Y+bMmRo0aJBH2i7JDz/8oB9++MGUvotz7733eqzt0ly1oixWr17t1f7MtnPnTt1yyy3220899ZTbrzohSStWrNB1112ndu3aacOGDZIKXn3BE5eEdGT//v36+eef9fjjj3utTwAwEyP0ASL/f6Bm8sQ3/+cGVJvNZr/Gsrv4Qkgzi78F+u+//97sEhDgunfvblqY9xXffPONfTTX3cz4e3v69Gn16tXL6/2aJSUlpdjXzp3n36ekpOjtt99Wu3bt7IvpnftZJCsry219ueK3335Tjx49TOnbV/jaLB8AnsUIfYAIlD/exU25ZzTevfxtwUezeeuKCIBZAuX/I/kC7XhK4q3jNQxDFotFNWrU8Ep/AIDiMUIfILz5wSUlJUXt2rXzygjO+Yv8ebq/8sTfRujNdv46BHC/8vz7CLgiP1RfddVVXulv//79ql69uiwWiy666CKvTqEvSmZmpqn9A4AvIdAHCG8G+kceecRj4WbOnDkeaReFEZ7ga7x9PnV51rFjR7NLQBkUd3lMdxo1apQsFosaN26s1NRUSWfDvRmXbwwLC/N6nwDgD5hyHyDK29RCd3nxxRfNLsE0UVFRZpcAAPAxJ0+eVGRkpHJycswuRcnJyYqIiDC7DL/DZ0KgfCHQBwj+eAMAEFi8/f92X/ssQZgHgJIxvzFA+Nr/hAEAgG/bv3+/duzYYXYZAIAyYIQ+QBDoAQBASQzD8Kn1KsrzqW+ewmdCoHwh0AMAAJQDP/30k0+E+TNnzqhKlSpml1EuXH311WaXAMDDzP+rDrfg21gAAOAPCPOedemll2r9+vV66KGHtGzZMrPLAeBhjNA7EB8fr/j4eFmtVrNLcQqBHgAA+KpXXnnF7BLKhauuukr333+/goKC9MEHH5hdDgAvINA7EBcXp7i4OKWlpSk8PNzsckpEoAcAAL7ivvvuM7uEcsdms/F5ECiHmHIfIP7880+zSwAAAJAk/fLLL2aXUC78/fffMgxDhmEQ5oFyikAPAAAA+KH69eubXQIAkxHoAQAAAADwQwR6AAAAOC0uLs7sEgAA/x+BHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAAA8EOGYZhdAgCTEegBAAAAAPBDBHoAAAAAAPwQgR4AAAAAAD9EoAcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAPBDXLYOAIEeAAAAAAA/RKAHAAAAAMAPEegdiI+PV8uWLRUTE2N2KQAAAAAAFGIxOPmmWGlpaQoPD1dqaqrCwsLMLschi8VidgkAAADwoszMTIWGhppdBgAPcDaHVvBiTQAAAADK6IEHHlC3bt0I8wAI9AAAAIA/SEpKUtWqVVW1alWzSwHgIziHHgAAAPBhP//8swzDUEREBGEeQAGM0AMAAAA+KiMjQxdccIHZZQDwUYzQAwAAAD6KhY8BFIdADwAAAACAHyLQAwAAAD6KEXoAxSHQAwAAAD6KQA+gOAR6AAAAAAD8EIEeAAAAAAA/RKAHAAAAfBRT7gEUh0APAAAA+CgCPYDiEOgBAAAAAPBDBHoAAAAAAPwQgR4AAADwUUy5B1AcAj0AAAAAAH6IQA8AAAD4KEboARSHQA8AAAD4KAI9gOIQ6AEAAAAA8EMEegAAAAAA/BCBHgAAAPBRTLkHUBwCPQAAAAAAfohADwAAAACAHyLQAwAAAD6KKfcAikOgBwAAAADADxHoAQAAAADwQwEf6Hfu3Km2bdvafypXrqx58+aZXVbAMAxDhmGYXQYAAAAAlDsBH+gvvvhibdq0SZs2bdLKlStVpUoV3XjjjWaX5bcWLlxo/3fPnj1NrAQAAAAAyrcKZhfgTQsWLNANN9ygKlWqmF2K30lJSVF4eLjZZQAAAAAA/j/TR+iXL1+uXr16KTIyUhaLpcjp8PHx8WrcuLFCQ0MVGxurNWvWlKqvr776Sn379i1jxeWPYRiEeQAAAADwMaYH+oyMDEVHRys+Pr7I+7/88kuNGDFCo0eP1oYNGxQdHa1u3brp2LFj9n3atm2r1q1bF/o5cuSIfZ+0tDStWrVKN998s8ePyd+9++67ZpcAAAAAACiB6VPue/TooR49eji8f8KECRoyZIgGDhwoSZo2bZoWLlyoGTNmaNSoUZKkTZs2ldjP/PnzddNNNyk0NLTY/bKzs5WdnW2/nZaW5sRRAAAAAADgXaaP0BcnJydH69evV9euXe3bgoKC1LVrV61evdqltpydbj9u3DiFh4fbf6KiolyuGwAAAAAAT/PpQH/ixAlZrVbVrVu3wPa6devq6NGjTreTmpqqNWvWqFu3biXu++yzzyo1NdX+c/DgQZfrBgAAAADA00yfcu8N4eHhSk5OdmrfkJAQhYSEeLgi71m4cKHq1KmjmJgYs0sBAAAAALiRT4/Q16pVS8HBwYXCeHJysiIiIkyqyn8MHz5cN998sy688EKzSylXDMPg/QkAAADA43w60FeqVEnt27fXkiVL7NtsNpuWLFmijh07mlhZYLNYLGaXAAAAUO5dc801ZpcAwMeZPuX+9OnT2rNnj/12YmKiNm3apJo1a6phw4YaMWKE+vfvryuuuEIdOnTQxIkTlZGRYV/13lPi4+MVHx8vq9Xq0X4AAACAcw0ZMkQTJkxQlSpVzC4FgI8zPdCvW7dOXbp0sd8eMWKEJKl///6aNWuW+vbtq+PHj+ull17S0aNH1bZtW/3888+FFspzt7i4OMXFxSktLU3h4eEe7Qv+49ixY7JarapXr57ZpQAAgADy4Ycfql27dmrSpAmfPQE4zfRA37lzZxmGUew+Q4cO1dChQ71UEeBYxYoVmbUBAADcbvDgwWaXAMAP+fQ59PBtnGsPAAAAAOYh0AMw1fPPP292CQAAeFVSUpLZJQAIEAR6AKYxDEOXX3652WUAAOA1zZs3N7sEAAGEQO9AfHy8WrZsqZiYGLNLAQAAAACgEAK9A3FxcUpISNDatWvNLgUAAAAAgEII9AAAAAAA+CECPQAAAAAAfohADwAAAHgJl/0F4E4EevitcePGmV0CAAAAAJiGQO8Aq9z7tg8//FCjRo0yuwyHDMMwuwQAAAAAAY5A7wCr3PuWzz//XFlZWfbbTFcDAAAAUN5VMLsAwBn33nuv2SUAAAAAgE9hhB4AAAAAAD9EoIfpfv31V+3cudPsMpzCVH8AAAAAvoJAD6+qX7++du3aVWBbbGysWrRoYVJFAAAAAOCfOIceXrVs2TI1a9bM7DIAAAAAwO8xQg+v2LNnjwzDIMx70R133GF2CQAAAAA8iEDvANehL5u+ffva//3bb7+padOmJlZTftx///2y2WwyDEOffvqp2eUAAAAA8CCm3DsQFxenuLg4paWlKTw83Oxy/IphGMrOztaXX35pdinlimEYZpcAAAAAwIsYoQcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIHegfj4eLVs2VIxMTFmlwIAAAAAQCEEegfi4uKUkJCgtWvXml0KAAAAAACFEOgBAAAAAPBDBHoAAAAAAPwQgR4AAAAAAD9EoAcAAAAAwA9VMLsAX2cYhiQpLS3N5Epcl52drbS0NKWnp7v0uMzMzAK3HR17bm5ukfelpaUpOzvbfjsjI6PY58+Z5/b8fTIzM015TdLS0pSVlVXiPjabzUsVFez3XBkZGV6vwVVpaWk6c+aM2WUAAOA1Vqu1yM9m/vhZE4Dn5P9NyM+jjliMkvYo5w4dOqSoqCizywAAAAAAlDMHDx5UgwYNHN5PoC+BzWbTkSNHVK1aNVksFrPLKVZMTEy5vsyevx6/r9VtRj2e7tMT7buzzbK2lZaWpqioKB08eFBhYWFuqQne4Wu//97mr8fvS3UH4t9sT/XhK3+3+Zvt33zp998M/nr8vlS3s7UYhqH09HRFRkYqKMjxmfJMuS9BUFBQsd+I+JLg4OBy/T8Gfz1+X6vbjHo83acn2ndnm+5qKywszKfeSyiZr/3+e5u/Hr8v1R2If7M91Yev/d3mb7Z/8qXffzP46/H7Ut2u1BIeHl7iPiyKF0Di4uLMLsFU/nr8vla3GfV4uk9PtO/ONn3tPQDvKe+vvb8evy/VHYh/sz3VB3+34Q7l/bX31+P3pbrdXQtT7gHAz6WlpSk8PFypqak+8+0zAKBo/M0G4E6M0AOAnwsJCdHo0aMVEhJidikAgBLwNxuAOzFCDwAAAACAH2KEHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAD4tZycHDVr1kyrVq1ya7s///yz2rZtK5vN5tZ2AQBwFwI9AAA+ZMCAAbJYLIV+9uzZY3ZpPmvatGm66KKLdNVVV9m3WSwWzZs3r9C+AwYM0O233+5Uu927d1fFihU1e/ZsN1UKAIB7EegBAPAx3bt3V1JSUoGfiy66qNB+OTk5JlTnWwzD0JQpUzR48GCPtD9gwABNmjTJI20DAFBWBHoAAHxMSEiIIiIiCvwEBwerc+fOGjp0qJ588knVqlVL3bp1kyRt27ZNPXr0UNWqVVW3bl098MADOnHihL29jIwMPfjgg6patarq1aunt99+W507d9aTTz5p36eoEe3q1atr1qxZ9tsHDx5Unz59VL16ddWsWVO33Xab9u3bZ78/f/T7rbfeUr169XThhRcqLi5Oubm59n2ys7P1zDPPKCoqSiEhIWrWrJk++ugjGYahZs2a6a233ipQw6ZNm4qdobB+/Xrt3btXPXv2dPFZlvbt21fkbIjOnTvb9+nVq5fWrVunvXv3utw+AACeRqAHAMCPfPzxx6pUqZL++OMPTZs2TSkpKbr++ut1+eWXa926dfr555+VnJysPn362B8zcuRI/f7775o/f75+/fVXLVu2TBs2bHCp39zcXHXr1k3VqlXTihUr9Mcff6hq1arq3r17gZkCS5cu1d69e7V06VJ9/PHHmjVrVoEvBR588EHNmTNHkyZN0vbt2/X++++ratWqslgsGjRokGbOnFmg35kzZ+q6665Ts2bNiqxrxYoVatGihapVq+bS8UhSVFRUgVkQGzdu1IUXXqjrrrvOvk/Dhg1Vt25drVixwuX2AQDwtApmFwAAAAr64YcfVLVqVfvtHj166Ouvv5YkNW/eXG+++ab9vldffVWXX365Xn/9dfu2GTNmKCoqSrt27VJkZKQ++ugjffbZZ7rhhhsknf1SoEGDBi7V9OWXX8pms+nDDz+UxWKRdDZsV69eXcuWLdNNN90kSapRo4amTJmi4OBgXXLJJerZs6eWLFmiIUOGaNeuXfrqq6+0aNEide3aVZLUpEkTex8DBgzQSy+9pDVr1qhDhw7Kzc3V559/XmjU/lz79+9XZGRkkffde++9Cg4OLrAtOzvbPpofHBysiIgISVJWVpZuv/12dezYUWPGjCnwmMjISO3fv9+FZwsAAO8g0AMA4GO6dOmiqVOn2m9XqVLF/u/27dsX2Hfz5s1aunRpgS8A8u3du1eZmZnKyclRbGysfXvNmjV18cUXu1TT5s2btWfPnkIj4VlZWQWmo7dq1apAiK5Xr562bt0q6ez0+eDgYHXq1KnIPiIjI9WzZ0/NmDFDHTp00Pfff6/s7GzdfffdDuvKzMxUaGhokfe988479i8O8j3zzDOyWq2F9h00aJDS09O1aNEiBQUVnMBYuXJlnTlzxmENAACYhUAPAICPqVKlisMp5ueGe0k6ffq0evXqpTfeeKPQvvXq1XN6dXyLxSLDMApsO/fc99OnT6t9+/ZFrvheu3Zt+78rVqxYqN38y75Vrly5xDoeeughPfDAA3rnnXc0c+ZM9e3bVxdccIHD/WvVqmX/wuB8ERERhZ7HatWqKSUlpcC2V199Vb/88ovWrFlT5NT9U6dOFThGAAB8BYEeAAA/1q5dO82dO1eNGzdWhQqF/7fetGlTVaxYUX/99ZcaNmwoSfr333+1a9euAiPltWvXVlJSkv327t27C4xKt2vXTl9++aXq1KmjsLCwUtXapk0b2Ww2/f7774VGzvPdfPPNqlKliqZOnaqff/5Zy5cvL7bNyy+/XFOnTpVhGPZTAVwxd+5cjR07Vj/99JOaNm1a6P78GQiXX365y20DAOBpLIoHAIAfi4uL06lTp3Tvvfdq7dq12rt3r3755RcNHDhQVqtVVatW1eDBgzVy5Ej99ttv2rZtmwYMGFBoWvn111+vKVOmaOPGjVq3bp0effTRAqPt999/v2rVqqXbbrtNK1asUGJiopYtW6bhw4fr0KFDTtXauHFj9e/fX4MGDdK8efPsbXz11Vf2fYKDgzVgwAA9++yzat68uTp27Fhsm126dNHp06f1999/u/CsnbVt2zY9+OCDeuaZZ9SqVSsdPXpUR48e1alTp+z7/PnnnwoJCSmxDgAAzECgBwDAj0VGRuqPP/6Q1WrVTTfdpDZt2ujJJ59U9erV7aF9/Pjxuvbaa9WrVy917dpV11xzTaFz8d9++21FRUXp2muv1X333aenn366wFT3Cy64QMuXL1fDhg1155136tJLL9XgwYOVlZXl0oj91KlT1bt3bz3++OO65JJLNGTIEGVkZBTYZ/DgwcrJydHAgQNLbO/CCy/UHXfcUeSpACVZt26dzpw5o1dffVX16tWz/9x55532febMmaP777+/2Gn/AACYxWKcf8IcAAAIeJ07d1bbtm01ceJEs0spZMWKFbrhhht08OBB1a1bt8T9t2zZohtvvFF79+4tcnHA0jpx4oQuvvhirVu3ThdddJHb2gUAwF0YoQcAAD4hOztbhw4d0pgxY3T33Xc7FeYl6bLLLtMbb7yhxMREt9azb98+vffee4R5AIDPYlE8AADgE+bMmaPBgwerbdu2+uSTT1x67IABA9xezxVXXKErrrjC7e0CAOAuTLkHAAAAAMAPMeUeAAAAAAA/RKAHAAAAAMAPEegBAAAAAPBDBHoAAAAAAPwQgR4AAAAAAD9EoAcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAQAAAADwQwR6AAAAAAD8EIEeAAAAAAA/RKAHAAAAAMAPEegBAAAAAPBDBHoAAAAAAPwQgR4AAAAAAD9EoAcAAAAAwA8R6AEAAAAA8EMEegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAcBPzJo1SxaLRfv27XNbm6dPn1ZQUJAmTJjgtjZd8eabb+qSSy6RzWYzpX8zTJs2TQ0bNlR2drbX+vSV57m0x+4r9ZdWeX7N4Rwz3iMAAgOBHgBK4b333pPFYlFsbKzZpZTJtm3bZBiGWrVq5fW+09LS9MYbb+iZZ55RUFD5+d/RgAEDlJOTo/fff98r/RX1PK9du1ZDhw5Vq1atVKVKFTVs2FB9+vTRrl27XG7flbZKc+yB8D7xhdc8X3Z2tp555hlFRkaqcuXKio2N1aJFi0ps8/Tp0xo9erS6d++umjVrymKxaNasWU7V89prr8lisah169ZubbcopT2+stTijuPz9nsEQODwz/8zAoDJZs+ercaNG2vNmjXas2eP2eWU2tatWyVJLVu29HrfM2bMUF5enu69916v922m0NBQ9e/fXxMmTJBhGB7vr6jn+Y033tDcuXN1ww036N1339XDDz+s5cuXq127dtq2bZtL7bvSVmmOPRDeJ77wmucbMGCAJkyYoPvvv1/vvvuugoODdfPNN2vlypXFtnnixAmNHTtW27dvV3R0tNO1HDp0SK+//rqqVKni1nYdKe3xlbYWdx2ft98jAAKIAQBwyT///GNIMr799lujdu3axpgxY7zS78yZMw1JRmJiotvaHDZsmBEWFua29lxx2WWXGf369TOlb7OtW7fOkGQsWbLE430V9Tz/8ccfRnZ2doFtu3btMkJCQoz777/fpfZdbcvVYw+U94nZr7lhGMZff/1lSDLGjx9v35aZmWk0bdrU6NixY7FtZmVlGUlJSYZhGMbatWsNScbMmTNLrKVv377G9ddfb3Tq1Mlo1aqV29otSlmOr7S1uPP4vPkeARA4GKEHABfNnj1bNWrUUM+ePdW7d2/Nnj270D5jxoyRxWLRnj17NGDAAFWvXl3h4eEaOHCgzpw5U2DfZcuW6YorrlBoaKiaNm2q999/3/54Zxw+fFiDBg1S3bp1FRISolatWmnGjBlOPXbr1q269NJLtWHDBvXo0UPVqlVT/fr19e677zr1+NJKTEzUli1b1LVr10L33XjjjerYsaNWr16tzp07q0qVKmrWrJl+/PFHSdKPP/6oK6+8UlWqVFHbtm21fv16t9fn6Rrat2+vmjVrav78+W6v/VyOnuerrrpKlSpVKrCtefPmatWqlbZv3+5SH6625cqxO6r/ww8/VGhoqK6++mrt37/fvt0wDHXp0kW1atXSsWPHXDqOorizH7Nfc0n65ptvFBwcrIcffti+LTQ0VIMHD9bq1at18OBBh+2GhIQoIiLCpVqWL1+ub775RhMnTnRru46U5fhKU4u7j89b7xEAgYVADwAumj17tu68805VqlRJ9957r3bv3q21a9cWuW+fPn2Unp6ucePGqU+fPpo1a5Zefvll+/0bN25U9+7ddfLkSb388ssaPHiwxo4dq3nz5jlVS3Jysq688kotXrxYQ4cO1bvvvqtmzZpp8ODBxX7IzLd161alp6frlltuUbt27fTWW2+pXr16+s9//mOfju8Jq1atkiS1a9eu0H1btmxRamqq+vbtqy5duuj1119Xenq67rvvPr3//vsaPny47rjjDr3wwgvau3evBg0a5Pb6vFFDu3bt9Mcffzi8Pzc3VydOnHDqx9HCZ8U9z+czDEPJycmqVauWy8fialslHXs+R/XHxMRo5MiR+vPPP/XWW2/Zt8fHx2vZsmWaPHmy6tSpU4Yj8Ew/Zr/mGzduVIsWLRQWFlZge4cOHSRJmzZtcvpYSmK1WjVs2DA99NBDatOmjdvaLU4gHJ+zvxsAkK+C2QUAgD9Zv369duzYocmTJ0uSrrnmGjVo0ECzZ89WTExMof0vv/xyffTRR/bbJ0+e1EcffaQ33nhDkjR69GgFBwfrjz/+UGRkpKSzXwJceumlTtXz/PPPy2q1auvWrbrwwgslSY8++qjuvfdejRkzRo888ogqV65c5GOTkpJ08uRJWSwWbdiwQVFRUZKk6667Ti1bttTGjRs99kF8x44dkqSLLrqowPZjx47p2LFjslgs2rhxo+rVqydJCgoK0vDhwzVx4kRt2LDB/oH9xIkTeuedd5Sdna2QkBC31OatGpo0aaJPP/3U4f1//PGHunTp4lRbiYmJaty4caHtjp7nosyePVuHDx/W2LFjneqzLG2VdOz5HNUfHR2t6OhorVmzxj474p9//tGoUaN0++23u+18e3f3Y/ZrnpSUZH8/nyt/25EjR5zq2xnTpk3T/v37tXjxYre1WZJAOD5nfzcAIB+BHgBcMHv2bNWtW9f+odtisahv37767LPP9Pbbbys4OLjA/o8++miB29dee62+++47paWlqUqVKlq8eLHuuOMOe5iXpGbNmqlHjx76/vvvi63FMAzNnTtXffr0kWEYOnHihP2+bt266YsvvtCGDRt09dVXF/n4LVu2SJLGjh1rD/OSVLFiRUkqNI3anU6ePKkKFSqoatWqRdY0ZsyYAh/M8/cbP358gdG38PBwBQUFuXX1c2/VUKNGDWVmZurMmTO64IILCt0fHR3t9Orcjqb0Onqez7djxw7FxcWpY8eO6t+/v1N9lqWtko7d2frbtm2rqVOnymazadCgQQoJCdHUqVPLVL8n+zH7Nc/MzCzyS6fQ0FD7/e5w8uRJvfTSS3rxxRdVu3Ztt7TpjEA4Pmd/NwAgH4EeAJxktVr1xRdfqEuXLkpMTLRvj42N1dtvv60lS5bopptuKvCYhg0bFrhdo0YNSdK///6rjIwMZWZmqlmzZoX6Kmrb+Y4fP66UlBRNnz5d06dPL3Kf4s7vzZ9Sf8cddxTYnj/Cd/HFF5dYg7vl13TrrbcW2L5z505VrlxZN954Y4Htu3btUtOmTe1fQkydOlUffPCBtm7dqueff15jxozxag3Z2dl67LHHtHjxYqWkpKhly5Z655131LFjx0L9GP9/JWtHayXUqFGjyPOg3e3o0aPq2bOnwsPD7ecge7qtko7dWa1bt1Z6erpGjhyp33//XZ9++mmBoOuO90NJ/fjTa165cuUir3OelZVlv98dXnjhBdWsWVPDhg1zS3vOCoTjc9fvBoDyg0APAE767bfflJSUpC+++EJffPFFoftnz55dKNCXFGjKIv8c2n79+jkcCb3sssscPn7r1q2qX79+oZG+zZs3q0KFCiVeym7AgAHq3LmzBgwY4Frhki688ELl5eUpPT1d1apVs2/fsmWL6tWrV2DGQn5NrVu3LjT6tnnz5gLHWK9ePY0ZM0aff/65yzW5o4a8vDw1btxYK1euVIMGDfTVV1+pV69e2rdvX6ER03///VcXXHCBw5CRk5OjU6dOOVVz7dq1i3yvOXqe86WmpqpHjx5KSUnRihUrCh2zK1xpq6Rjd7b+/Ot+T5gwQbfccov69etX4H53vB9K6sefXvN69erp8OHDhR6TlJQkSWV6/fPt3r1b06dP18SJEwtMcc/KylJubq727dunsLAw1axZs8x9nS8Qjs/Z3w0AyEegBwAnzZ49W3Xq1FF8fHyh+7799lt99913mjZtmtMfxOrUqaPQ0NAir2PvzLXta9eurWrVqslqtZZqVG/r1q1FBv4tW7aoRYsWbjsnvSiXXHKJpLPnAZ9bw5YtW4q8VvPmzZvVs2fPAttyc3O1c+dO9enTx77t9ttvlyT7avSlUZYaqlSpopdeesl+/z333KMRI0Zo586dat++fYHHJiYmFrtWwqpVq8p8PrWj51k6G0B69eqlXbt2afHixSV+gVMcV9sq6didqV/6v1kk1atX1/vvv1/ofne8H0rqx59e87Zt22rp0qVKS0srcNrIX3/9Zb+/rA4fPiybzabhw4dr+PDhhe6/6KKL9MQTTzi1aKerAuH4nP3dAIB8BHoAcEJmZqa+/fZb3X333erdu3eh+yMjIzVnzhwtWLBAffv2darN4OBgde3aVfPmzdORI0fso0d79uzRTz/95NTj77rrLn3++efatm2bfRQx3/Hjxx2e32m1WrV9+3Z169at0H2bN2/W5Zdf7tQxlFb+dOR169bZQ4fValVCQkKhKe0nTpxQUlJSoZC9fft25ebmFjsLwVXurmH37t06depUkadQbNiwQffff7/DWtxxPnVRz7N09jj79u2r1atXa/78+UVOD3dWadoq6dhLqj/fBx98IOns6RHuGH11xJV+fPU1l6TevXvrrbfe0vTp0/X0009LOnvKwMyZMxUbG2tfS+PMmTM6cOCAatWq5fJVD1q3bq3vvvuu0PYXXnhB6enpevfdd9W0aVOX2nRWIByfs78bAJCPQA8ATliwYIHS09MLnVed78orr1Tt2rU1e/ZspwO9dHbhtV9//VVXX321HnvsMVmtVk2ZMkWtW7d26hJL//vf/7R06VLFxsZqyJAhatmypU6dOqUNGzZo8eLFDqfv7t69W1lZWYU+8GdmZmrPnj1lXhitJE2aNFHr1q21ePFi+yXf8ms6PzRv3rxZkgptz1+8ztVAb7FY1KlTJy1btqzQfe6sITMzU/369dOzzz6r8PDwAvetX79ep06d0m233eawTnecT13U8yxJTz31lBYsWKBevXrp1KlT+uyzzwo87vyp68U9Z6625cyxl1S/JO3du1fPPfecpP9b96E0ijs2V/vx5ddcOrvex913361nn31Wx44dU7NmzfTxxx9r3759Ba7GsWbNGnXp0kWjR48usO7AlClTlJKSYp9q/v333+vQoUOSpGHDhik8PFy1atWyz4w4V/6IdVH3OdOuVPJrVdbjc6YWTx6fK78bAGBnAABK1KtXLyM0NNTIyMhwuM+AAQOMihUrGidOnDBGjx5tSDKOHz9eYJ+ZM2cakozExET7tiVLlhiXX365UalSJaNp06bGhx9+aDz11FNGaGhoiY81DMNITk424uLijKioKKNixYpGRESEccMNNxjTp093WOtXX31lSDK2bdtWYPuaNWsMScYPP/xQ5ON69uxphIeHG+Hh4UbFihWNypUr22+PGzfOYX9FmTBhglG1alXjzJkzxdY0YcIEQ5KRkpJSYPt///tfIywszLDZbIXafuSRR4zRo0cX2p6enm5IMu65554ia3JXDTk5OUbPnj2N++67r8j6nnnmGaNhw4ZF3udu5z/PhmEYnTp1MiQ5/DlXSc+ZK20ZhuvHXlT9NpvN6NSpk1GjRg1j4MCBRtWqVYttr7TvB1f68fXXPF9mZqbx9NNPGxEREUZISIgRExNj/PzzzwX2Wbp0qSGp0HPWqFEjh6/z+X+XztepUyejVatWRd7nTLslvVbuOL6yHGNZj88wvPseARA4CPQA4INuu+02o1mzZmaXUaz+/fsbM2fOLPXjU1JSjJo1axoffvih+4r6/xwFuIULFxoWi8XYsmWL2/vMZ7Vajb59+xq33HKLkZubW+j+rKwsIyIiwpg4caLHajhXWZ9ndz5npTn2ouqfMmWKIcn45JNPjDlz5hiSjL179zpso7TvB2f7CbTX3Bd543fXTN5+jwAIHO67cC8AoFTOvzby7t279eOPP6pz587mFOQl4eHh+u9//6vx48fbV+wvq7y8PGVlZclqtRb4d76lS5fqnnvuUZs2bdzSX1EeeeQRJSUl6euvv1aFCoXPbJs5c6YqVqyoRx991GM1nKusz7M7n7PSHPv59e/bt0+jRo1Sr1699MADD9jr2rBhQ6HHluX94Eo/gfaa+yJv/O6aydvvEQCBw2IYbrh2EgCg1OrVq6cBAwaoSZMm2r9/v6ZOnars7Gxt3LhRzZs3N7s8h8py2TpPGTNmjF5++eUC22bOnOm1Gvfv36/GjRsrNDS0wCXFfvrpJ1177bVeqSGQGYahrl27auPGjfr7779Vr1495eXlqUaNGoqMjNRTTz2l+++/X1WqVJFU+veDK/3wmgMAzESgBwCTDRw4UEuXLtXRo0cVEhKijh076vXXX1e7du3MLg3wKe+//74effRRffLJJ3rggQfs22fNmqUXX3xRx48fV3p6uipWrOgX/QAAUFYEegAAAAAA/BDn0AMAAAAA4IcI9AAAAAAA+KHCS7GiAJvNpiNHjqhatWqyWCxmlwMAAAAACHCGYSg9PV2RkZEKCnI8Dk+gL8GRI0cUFRVldhkAAAAAgHLm4MGDatCggcP7CfQOxMfHKz4+Xnl5eZLOPpFhYWEmVwUAAAAACHRpaWmKiopStWrVit2PVe5LkJaWpvDwcKWmphLoAQAAAAAe52wOZVE8AAAAAAD8EIHegfj4eLVs2VIxMTFmlwIAAAAAQCFMuS8BU+4BAAAAAN7ElHsAAAAAAAIYgR4AAAAAAD9EoAcAAAAAwA8R6B1gUTwAAAAAgC9jUbwSsCgeAAAAAMCbWBQPAAAAAIAARqAHAAAAAMAPEegBAAAAAPBDBPoAwnIIAAAAAFB+EOgd8LdV7h988EFFR0crJyfH7FIAAAAAAF7AKvcl8JdV7i0WiyTphx9+UM+ePU2uBgAAAABQWqxyX07x/QwAAAAAlA8EegAAAAAA/BCBHgAAAAAAP0SgBwAAAADADxHoAwzn0AMAAABA+UCgBwAAAADADxHoHfC369ADAAAAAMoXAr0DcXFxSkhI0Nq1a80uBQAAAACAQgj0AAAAAAD4IQI9AAAAAAB+iEAfYFjlHgAAAADKBwI9AAAAAAB+iEAPAAAAAIAfItAHGKbcAwAAAED5EPCBPiUlRVdccYXatm2r1q1b64MPPjC7JAAAAAAAyqyC2QV4WrVq1bR8+XJdcMEFysjIUOvWrXXnnXfqwgsvNLs0AAAAAABKLeBH6IODg3XBBRdIkrKzs2UYBtPSAQAAAAB+z/RAv3z5cvXq1UuRkZGyWCyaN29eoX3i4+PVuHFjhYaGKjY2VmvWrHGpj5SUFEVHR6tBgwYaOXKkatWq5abqAQAAAAAwh+mBPiMjQ9HR0YqPjy/y/i+//FIjRozQ6NGjtWHDBkVHR6tbt246duyYfZ/88+PP/zly5IgkqXr16tq8ebMSExP1+eefKzk52SvHBgAAAACAp5h+Dn2PHj3Uo0cPh/dPmDBBQ4YM0cCBAyVJ06ZN08KFCzVjxgyNGjVKkrRp0yan+qpbt66io6O1YsUK9e7du8h9srOzlZ2dbb+dlpbm5JH4Bk4nAAAAAIDywfQR+uLk5ORo/fr16tq1q31bUFCQunbtqtWrVzvVRnJystLT0yVJqampWr58uS6++GKH+48bN07h4eH2n6ioqLIdBAAAAAAAHuDTgf7EiROyWq2qW7duge1169bV0aNHnWpj//79uvbaaxUdHa1rr71Ww4YNU5s2bRzu/+yzzyo1NdX+c/DgwTIdAwAAAAAAnmD6lHtP69Chg9NT8iUpJCREISEhnivIw5hyDwAAAADlg0+P0NeqVUvBwcGFFrFLTk5WRESER/uOj49Xy5YtFRMT49F+AAAAAAAoDZ8O9JUqVVL79u21ZMkS+zabzaYlS5aoY8eOHu07Li5OCQkJWrt2rUf7AQAAAACgNEyfcn/69Gnt2bPHfjsxMVGbNm1SzZo11bBhQ40YMUL9+/fXFVdcoQ4dOmjixInKyMiwr3rvKfHx8YqPj5fVavVoPwAAAAAAlIbFMPmk62XLlqlLly6Ftvfv31+zZs2SJE2ZMkXjx4/X0aNH1bZtW02aNEmxsbFeqS8tLU3h4eFKTU1VWFiYV/osDYvFIkn69ttvdccdd5hcDQAAAACgtJzNoaaP0Hfu3LnEhdyGDh2qoUOHeqkiAAAAAAB8n0+fQ28mf10Uj1XuAQAAAKB8INA74K+L4hHoAQAAAKB8INAHGAI9AAAAAJQPBHoHmHIPAAAAAPBlBHoH/HXKvc1mM7sEAAAAAIAXEOgDjK+M0E+aNEm33XabcnJyzC4FAAAAAAISgT7A+Eqgf+KJJ7RgwQJ9+umnZpcCAAAAAAGJQO+Av55D72tT7k+fPm12CQAAAAAQkAj0DvjrOfS+MkIPAAAAAPAsAn2AIdADAAAAQPlAoA8wvhbofa0eAAAAAAgUBHoH/PUcegI0AAAAAJQPBHoHOIceAAAAAODLCPQBxtdWuQcAAAAAeAaBPsD42gi9xWIxuwQAAAAACEgE+gDja4He1+oBAAAAgEBBoA8wTLkHAAAAgPKBQO8Aq9wDAAAAAHwZgd4BVrkHAAAAAPgyAn2AIdADAAAAQPlAoA8wF1xwgdklAAAAAAC8gEAfIDp37ixJCg0NNbcQAAAAAIBXEOgDRP713plyDwAAAADlA4E+QAQFnX0pCfQAAAAAUD4Q6ANE/gg916EHAAAAgPKBQB8gGKEHAAAAgPKFQO9AfHy8WrZsqZiYGLNLcYovjdDn5uba/+0L9QAAAABAICLQOxAXF6eEhAStXbvW7FKc4ksj9IcOHbL/OzMz08RKAAAAACBwEegDhC+N0AMAAAAAPI9AHyB8aYT+XL5WDwAAAAAECgJ9gPDVEXqr1Wp2CQAAAAAQkAj0AcJXR+h97QsGAAAAAAgUBPoA4asj9L72BQMAAAAABAoCfYDID/S+EKDPrcEX6gEAAACAQFRuAv2ZM2fUqFEjPf3002aX4hH5U+59YYT+3BB/+PBhEysBAAAAgMBVbgL9a6+9piuvvNLsMjzGl0boz7VhwwazSwAAAACAgFQuAv3u3bu1Y8cO9ejRw+xSPMZXR+hbtGhhYiUAAAAAELhMD/TLly9Xr169FBkZKYvFonnz5hXaJz4+Xo0bN1ZoaKhiY2O1Zs0al/p4+umnNW7cODdV7Jt8aYT+3Bp84QsGAAAAAAhEpgf6jIwMRUdHKz4+vsj7v/zyS40YMUKjR4/Whg0bFB0drW7duunYsWP2fdq2bavWrVsX+jly5Ijmz5+vFi1aBPxIsa+O0PtCPQAAAAAQiCqYXUCPHj2KnQo/YcIEDRkyRAMHDpQkTZs2TQsXLtSMGTM0atQoSdKmTZscPv7PP//UF198oa+//lqnT59Wbm6uwsLC9NJLLxW5f3Z2trKzs+2309LSSnFU3scIPQAAAACUL6aP0BcnJydH69evV9euXe3bgoKC1LVrV61evdqpNsaNG6eDBw9q3759euuttzRkyBCHYT5///DwcPtPVFRUmY/DG3xphP5cvlYPAAAAAAQKnw70J06ckNVqVd26dQtsr1u3ro4ePeqRPp999lmlpqbafw4ePOiRftyNEXoAAAAAKF9Mn3LvTQMGDChxn5CQEIWEhCg+Pl7x8fGyWq2eL8wN8kfot27danIlBQO9L3zBAAAAAACByKdH6GvVqqXg4GAlJycX2J6cnKyIiAiP9h0XF6eEhAStXbvWo/24y7p16yRJn3zyicmVSB9//LH93wcOHDCxEgAAAAAIXD4d6CtVqqT27dtryZIl9m02m01LlixRx44dPdp3fHy8WrZsqZiYGI/24y5///232SXYvfHGG/Z/b9u2zcRKAAAAACBwmT7l/vTp09qzZ4/9dmJiojZt2qSaNWuqYcOGGjFihPr3768rrrhCHTp00MSJE5WRkWFf9d5T4uLiFBcXp7S0NIWHh3u0LwAAAAAAXGV6oF+3bp26dOlivz1ixAhJUv/+/TVr1iz17dtXx48f10svvaSjR4+qbdu2+vnnnwstlFfeWSwWzlcHAAAAgHLEYpACi3Tuoni7du1SamqqwsLCzC7LoQoVKtgX8DP7Jc1fcT+f2fUAAAAAgD/JnyleUg4l0JfA2SfSbJUqVVJubq4k8wM0gR4AAAAASs/ZHOrTi+LBeeeGaAI0AAAAAAQ+Ar0D/rbK/bmB/vvvvzexEgAAAACANxDoHfC369AHBf3fS3ns2DETKwEAAAAAeAOBPkCcG+gBAAAAAIGPFBggCPQAAAAAUL6QAh3w53PoAQAAAACBj0DvgL+dQ39uoCfcAwAAAEDgI9AHCKbcAwAAAED5QgoMEIzQAwAAAED5QqAPEOeO0BuGYWIlAAAAAABvINA74G+L4jHlHgAAAADKF1KgA/68KB4AAAAAIPAR6APEuYGeKfcAAAAAEPgI9AHi3Cn3Q4YMMbGSwmw2m9klAAAAAEDAIdAHCF8+h54ZAwAAAADgfr6bAuGS88+h96UQzQg9AAAAALgfgd4Bf1/l3qxAf/r06ULbEhISTKgEAAAAAAIbgd4Bf1/l3qxAP3r06ELb2rZt6/1CAAAAACDAEegDhK+M0G/ZssWUfgEAAACgvCHQB4jzR+jNOm998eLFRW7Pzc31ciUAAAAAENgI9AHi/BH6d955x6RKijZr1iyzSwAAAACAgEKgDxDnB/pRo0aZVEnRTp06ZXYJAAAAABBQCPQB4vwp92bIzMx0eB+XrgMAAAAA9yLQw21ee+01h/c999xzXqwEAAAAAAIfgd4Bf7sOvS/YuHGj2SUAAAAAQLlBoHfA369DX15rAAAAAIDygkAfIAjTAAAAAFC+EOgDhGEYZpdQIq5FDwAAAADuQ6CH2yxcuLDY+48cOeKlSgAAAAAg8BHo4RZWq9XsEgAAAACgXCHQwy38Yco/AAAAAAQSAn2AMDtQsygfAAAAAHhXBbML8IbGjRsrLCxMQUFBqlGjhpYuXWp2SeUSoR8AAAAA3KdcBHpJWrVqlapWrWp2GR5TVFi+7bbbNHPmTNWsWdOEigAAAAAAnsSU+wBR1JT7BQsW6MILLzShmqL98ccfZpcAAAAAAAHD9EC/fPly9erVS5GRkbJYLJo3b16hfeLj49W4cWOFhoYqNjZWa9ascakPi8WiTp06KSYmRrNnz3ZT5b7F7HPoFy1aVOI+9913nxcqAQAAAIDywfQp9xkZGYqOjtagQYN05513Frr/yy+/1IgRIzRt2jTFxsZq4sSJ6tatm3bu3Kk6depIktq2bau8vLxCj/31118VGRmplStXqn79+kpKSlLXrl3Vpk0bXXbZZR4/Nm8y+/z0Hj16mNo/AAAAAJQ3FsPsod1zWCwWfffdd7r99tvt22JjYxUTE6MpU6ZIkmw2m6KiojRs2DCNGjXK5T5GjhypVq1aacCAAUXen52drezsbPvttLQ0RUVFKTU1VWFhYS735y2XXHKJdu7cWeR93niJnf1CoVOnTpo4caLatm3r2YIAAAAAwE+lpaUpPDy8xBxq+pT74uTk5Gj9+vXq2rWrfVtQUJC6du2q1atXO9VGRkaG0tPTJUmnT5/Wb7/9platWjncf9y4cQoPD7f/REVFle0gUMDvv/9e4PUEAAAAAJSOTwf6EydOyGq1qm7dugW2161bV0ePHnWqjeTkZF1zzTWKjo7WlVdeqQcffFAxMTEO93/22WeVmppq/zl48GCZjqE8cHVNg5MnT3qoEgAAAAAoP0w/h97TmjRpos2bNzu9f0hIiEJCQhQfH6/4+HhZrVYPVhcYYmNjzS4BAAAAAModnx6hr1WrloKDg5WcnFxge3JysiIiIjzad1xcnBISErR27VqP9uMuNpvN7BIAAAAAAF7k04G+UqVKat++vZYsWWLfZrPZtGTJEnXs2NHEylBWGRkZWrp0aZFXJwAAAAAAlMz0QH/69Glt2rRJmzZtkiQlJiZq06ZNOnDggCRpxIgR+uCDD/Txxx9r+/bteuyxx5SRkaGBAwd6tK74+Hi1bNmy2PPtUXp33HGHrr/+er322mtmlwIAAAAAfsn0y9YtW7ZMXbp0KbS9f//+mjVrliRpypQpGj9+vI4ePaq2bdtq0qRJXjtv29nLBZitRYsW2r17d5H3/fPPP7rooos81rezl6wriisLHAIAAABAeeBsDjV9UbzOnTuXeJ30oUOHaujQoV6q6Cx/WxSvuOewSZMmOnPmjCpXruz2fr/88ssyPd7k75MAAAAAwG+ZPkLv6/xlhL5Zs2bau3evw/sPHTqk+vXru73fsozOS1KdOnUKLXoIAAAAAOWZsznU9HPo4R19+/bV6dOnzS6jEL5PAgAAAIDSIdAHiJJGyv/44w+NGzfOS9U47/jx42aXAAAAAAB+iUDvQCCucn/s2DGzSyjSwIEDtXz5crPLAAAAAAC/wjn0JfCXc+ibN2+uPXv2FLvPbbfdpnnz5rm137KeQ38u3ooAAAAAwDn05Y4zYXj+/Plu7XPGjBlubW/79u1ubQ8AAAAAAhmB3gF/m3Jvs9mc2s+do+CDBw92W1uSlJqa6tb2AAAAACCQMeW+BP4y5f6iiy7Svn37Stxv165dat68uVv6dOd0+/z2nP1iAgAAAAACFVPuy5moqCiv9vf888+7vU3DMLgmPQAAAAA4iUAfIC6++GKn9svOzi5zX1arVa+//nqZ2ylKSQv7AQAAAADOItCXM23atCnT4/Py8lShQgU3VVO0ZcuW6dChQx7tAwAAAAD8HYHeAX9bFM+VpRDKsmzC6tWrS/1YZ6xcuVJdunTx+ikEAAAAAOBvCPQOxMXFKSEhQWvXrjW7FLcLCgrSpk2bSvXYiRMnurWW87311lsebR8AAAAAAgWBPkC4uuJ8u3btStXPt99+W6rHOevEiRP2fz/yyCNKSkryaH8AAAAA4K8I9OWUYRhFTr1PSUnRhAkTlJCQoNTUVI0fP1779+83oUJp+vTpeuCBB0zpGwAAAAB8nWdXN4NPCwoK0vHjx1WrVi1J0uHDh9WgQQNJ0lNPPWXf77///a9SUlIUHh7u9RqXLFni9T4BAAAAwB8wQl/OjRs3zv7vuLg4h/tVr17d5baffPLJUlRU2IYNG9zSDgAAAAAEEgK9A/62yn1pTZgwwb5A3vz5893a9vPPP++Wdnbv3u2WdgAAAAAgkJQq0K9YsUL9+vVTx44ddfjwYUnSp59+qpUrV7q1ODMF8ir357v88su1a9euEvc7fvy4S+26ulAfAAAAAMB5Lgf6uXPnqlu3bqpcubI2btyo7OxsSVJqaqpef/11txcI73DmXPXy8OUGAAAAAPgLlwP9q6++qmnTpumDDz5QxYoV7duvvvpqznUOcDt27HBpf0boAQAAAMBzXA70O3fu1HXXXVdoe3h4uFJSUtxRE0qhrOH58ccfL3Gfc1e+97Z///1XWVlZpvUPAAAAAL7G5UAfERGhPXv2FNq+cuVKNWnSxC1FwXVFXVPebO4aof/2229Vs2ZN1atXzy3tAQAAAEAgcDnQDxkyRE888YT++usvWSwWHTlyRLNnz9bTTz+txx57zBM1wk+5K9B/9dVXksQMEAAAAAA4RwVXHzBq1CjZbDbdcMMNOnPmjK677jqFhITo6aef1rBhwzxRIwAAAAAAOI/Lgd5isej555/XyJEjtWfPHp0+fVotW7ZU1apVPVGfaeLj4xUfHy+r1Wp2KU5hAToAgK84duyYdu3apePHjysyMlI1atRQdna2srKyVLNmTeXk5Cg8PFyVK1eW1WqVYRjKy8uTzWZTcHCwwsLCZLPZZLPZZBiGKlWqpKCgs5MKDcNQUFCQgoODlZubq4oVK8owDPv/B4OCggrcLk7+PvmnrZ3/GGfbAQB3Of80Wkd/h/L3y7/fZrMV+JuW/2Oz2SSd/fuWk5Nj31ahQgUFBQUpOztbFotFwcHBys7OVlBQkHJycpSWlqaKFSuqQoWzcbFChQoKCQmx/43OysrSv//+q5ycHO3bt08HDhzQ0aNHFRQUpKCgIDVr1kz//POPDMPQgQMHdPLkSSUnJ8tisejCCy/UqVOnZBiGqlatqsTERG3YsEENGzZU1apVtWPHDnvdnmK1Wu3/X/F3Lgf6fJUqVVLLli3dWYtPiYuLU1xcnNLS0hQeHm52OX7JUx+CVq9erd27d+vBBx/0SPso33Jzc1WhQgWn37/79+9XVFSULBaL04+xWq3Ky8vTli1b1K5dO2VmZiooKEgXXHBBWUq3K83frejoaB0+fFhXX321Fi9erBtuuEHh4eGqXr26brjhBrVo0UKhoaGKjIzUmTNnZLPZdOGFF9r/55//447f+xYtWmj37t1lbsfXhIWFKSQkRK1bt9ZFF12kNm3a6Ndff1WNGjVUsWJFVa9eXV26dJHFYlF6eroSExN155136tChQ/YPQFarVaGhoQoODlZGRobq1q2rtLQ01apVSxUqVFDVqlUVHPz/2rv3sCjrff//r2FUQEQQSQ4JYmoaoqCoaGWpoUhmujtoZSlq2gF1F9VKVyt17226W5l5mZOuZZkdzI1elZ0uT5siLDUORVlapgvNUlDzgKCCwvz+8Ov8ZCMywAz3zPB8XNdcl3PPPZ/P+wades3ncJt14cIF299Js9kss9ksqer/+F36fdUUZuvrm2++Uf/+/R3SFgCg6fjtt98arS+z2eySe5DVh8lqx5Xcdddddjf4wQcfNKggV3Ppf4xPnTql1q1bG11OjaZOnaoVK1YYXUYVJ0+eVGBgYK3ntWrVSiUlJXa1efm3lNu2bdOAAQMaUiKczGq16l//+pfat2+vLVu26NZbb5Wvr6/MZrPt93j+/HmdPXtWpaWlkmTb/NBqterPP/9U9+7ddffdd2vRokXy9vbW4cOH1aZNG+3du1e+vr7q0qWLrb8OHTpo6tSpuvPOO9WuXTsdP35cHTt2lI+PzxXr69mzp3bv3q1FixYpMDBQ/v7+Gj169FWvacyYMfr4449144036vPPP3fAT8mxHnvsMS1btszoMgAAAFyaqwd6e3OoXYF+4sSJdnf85ptv2n2uOyDQ119xcXGtP7Phw4dr+fLlioqKsqvNywP922+/rYceeqihZTZpZWVlevXVV5WQkKD4+HjbCLHVatUzzzyjTp06KTk5WQcOHFC/fv00f/58tW3bVmPGjFFmZqbi4+PVpk0bTZ06VR999FGVtqOjo7Vr164a+x41alS19wAAAADO1qNHD/3www9Gl3FV9uZQu6bce1pIh/OZzWb5+/vbdW5QUJDd7b788sv1Lcll1TbFu6CgQHv27NGtt94qHx8fWa1WHT16VO3atbO938vLS7/88osiIiLk7++v7OxsffDBB3rwwQd1+PBhXXvttTp58qReeOEFXXPNNZo5c6YOHjyoYcOGVeuvTZs2OnHixFVrfvLJJ2u9rquFeUmEeaAJeP755/Xbb7/p0KFDOnbsmNq0aaN27dqpsrJSZ8+eta2trKio0G+//SaTyaTi4uJq7XTp0qVOyzA6dOigAwcOOPJSAKBJ69GjhwYNGqTExEQlJSWpoqJCe/fu1eHDh1VUVKTjx4/r8OHDKi0tVXl5ua677jqVlpbq5MmTslqtKisr0+nTp2W1WtW1a1e1bt1aZWVlki4OJPn6+qp58+YKDQ3VhQsXZLVa1bx5c5lMJvn6+srX11deXl629zVr1kwtWrSQj4+PvLy8VFlZKW9vb1mtVrVs2dK214rVapWPj49tLwBJVfZl8QT1XkMPXM2SJUvsOm/AgAF2B39Jevrpp21/dteNiqxWq3bt2qXOnTvLarWqY8eO6tKli7KysiRJX375pZo1a6aQkBCVlZUpJibG9t5169bp3nvvtbuvF1988YrH33rrrRrfU1uYh3s5evSo2rRpY9sMp6SkRHv27NGBAwfUvn17ZWZmqmPHjjp9+rRWr16toKAgffLJJ0aXLUk6ePCg2rdvb3QZkqpvmlZRUWH7HwWr1apz587ZZhCVlpbKZDLp7Nmzatmypfbv3y8vLy/9+eeftmO5ubkym83q2rWrTCaTTpw4oYKCAgUEBCg9PV3t27dXq1atlJ6eLn9/f1VWVsrPz09HjhzRmTNnjPxR2C09PV1jxowxugwAgJP07NlTPXv2NLqMJs+uKfeX69Wr1xWDlMlkko+Pjzp37qyUlBQNHjzYYUUaiSn39WOxWPT444/XGrrPnTsnb29v/fu//7vdXwJc8s477+jBBx9sSJlOcWnEqUOHDtVey8jIUGJiou35qlWrlJKS0ojVwR5TpkzRxo0bdfDgwVrPDQkJ0a+//io/Pz9JF7+Q+eOPP9SlSxctXrxYkyZN0oYNGxQUFKRly5bp0KFDioyM1P79+2vctfbyXWqPHj0qq9UqPz8/lZeX6/Tp09q4caPatGmjrVu3aunSpbr++uu1Z88eSVJERIQOHjyo6Ohofffdd2rRooUDfzIAAABoDA5dQ3+5WbNmadmyZerRo4f69esnScrJydEPP/yglJQU7dq1SxkZGfrggw80atSohl2FCyDQ1489gT46Olo//fSTJOlf//qXOnXqVKc+5s+fr1mzZjWoTke6FMQuv+bnnntO//Ef/6Hnn39en376qXbu3Glghe5rxowZqqys1OHDh/W///u/OnXqlO21l156SVOmTLHt6v77779r69at6ty5s9auXavu3bsrMjJSQ4YMqdbu2bNn5evrK+nizIT9+/crLi7ObWd/AAAAwDM4LdBPmTJFkZGRev7556scnzdvng4cOKAVK1Zozpw5+uyzz5Sbm1u/6l2IuwT6Rx55RP/85z+NLsOmroFeurhBW027kdfEVXanLCkpkb+/v2644Qbt3r3b6HJczjvvvKPvvvtOycnJatmypUJCQlRYWKiYmJgab6928uRJeXt7y8fHh4ANAACAJsWhm+Jdbu3atcrLy6t2/L777lN8fLxWrFih+++/X4sWLapr005TUFCgSZMmqaioSGazWTt27LBNj/UUrhJsL6lPAPP29nZCJc6zbds2Wa1WrV271rZcoCmE+TNnzsjX11dnz56VyWTShQsXtHLlSqWkpFz1w+b/Lo+obUaGPbc8BAAAAJqyOgd6Hx8fbdu2TZ07d65yfNu2bbbR1crKyjqPtDpTSkqK5s2bp4EDB+r48eNuFxzdkat9weAoVqtVzz33nBYsWGB0KfUSEhJi22Tr3Xff1SuvvKLdu3erVatW2rt3rz744APddNNNuuGGG9SzZ0+dP39eBw4cUNu2bfX777/rhhtusO0SemmqunRxSjwAAACAxlXnQD99+nQ9+uijysvLU9++fSVdXEP/+uuv669//askadOmTYqLi3NoofX1008/qXnz5ho4cKCkut0iDa7v/fff19133+2Uti/fnEySNm/erDvuuEPnz593Sn+O9tZbb6lPnz5q3769Dhw4oA8//FBPP/207V7zvXv3rjKTpn379ho0aFCVNpo3b2778q5NmzaNVjsAAACA2tX5Bnx/+9vftGLFCmVnZ2vGjBmaMWOGsrOztWLFCj333HOSpEcffdTu2x5lZWVp5MiRCg8Pl8lk0vr166udY7FYFBUVJR8fHyUkJCg7O9vuen/99Ve1atVKI0eOVO/evTV//ny734v6q8ut6C5X1x3f77nnnnr1cyVnzpxRZmamLly4oIULF+qaa67R999/r0ceeUQjRoxQUlKSS4b5uXPn6sSJE7bbZ116jB8/XtHR0WrdurV69Oih2bNn28I8AAAAAPdXr/vQjxs3TuPGjavx9cun4tamtLRUsbGxmjRpku66665qr6enpystLU3Lly9XQkKCFi9erKSkJP3yyy9q166dJCkuLk4XLlyo9t7NmzfrwoUL2rp1q/Lz89WuXTsNHz5cffv21dChQ+2u0R242qZhXl61f1c0duzYasdee+01rVq1ygkVVTdt2jRZrVZZLBZJF/eB+OSTTzR37lzNnTtXklxmpsmRI0dsf9+li2v1mzdvruuuu87lfvcAAAAAGkedd7m/pLy8XEeOHFFlZWWV45GRkfUvxmTShx9+qNGjR9uOJSQkqG/fvlq6dKmki+vzIyIiNH36dM2cObPWNrdv3665c+dq06ZNki7e4kqSnnnmmSueX1ZWprKyMtvz4uJiRUREuPwu965227rVq1frgQceuGrYLC8vV/Pmzasdr2tAXbx4scaNG6fg4GC733PixAnb8otjx46pbdu2LhOM582bp/vuu0/nz59Xjx49tGnTJg0ZMkTffPONjh49qnbt2tluGQkAAADA89i7y32dp9z/+uuvGjhwoHx9fdWhQwd17NhRHTt2VFRUlDp27Nigov+v8vJy5eXlKTEx8f8v2MtLiYmJ2r59u11t9O3bV0eOHNGJEydUWVmprKws3XDDDTWev2DBAgUEBNgeERERDb6Opsie74muFObr44knntC1115bp/dUVFTY/nzu3Lkrzg5pDPfcc48qKir01FNP2Y6NGDFCnTp1Urdu3XT+/Hnb/dMTEhJ0xx13EOYBAAAASKrHlPuUlBQ1a9ZMn376qcLCwpw6qnns2DFVVFQoJCSkyvGQkBD9/PPPdrXRrFkzzZ8/X7fccousVquGDRumO+64o8bzZ82apbS0NNvzSyP0cG3l5eV2n1tWVqZjx47Znrdv394ZJdll3bp1kqSFCxfq7rvv1p9//uky0/wBAAAAuLY6B/r8/Hzl5eWpW7duzqjHKZKTk5WcnGzXud7e3vL29pbFYpHFYqkykgvPcP311+u3335r1D579OihnTt3XvWcAQMGNFI1AAAAADxBnafcR0dHVxnddKbg4GCZzWYVFRVVOV5UVKTQ0FCn9p2amqpdu3YpJyfHqf04iqfe990ZGivMX34LuKysLO3fv7/K6y+++GKj1AEAAADAM9U50L/44ov6y1/+oszMTP35558qLi6u8nCkFi1aKD4+XhkZGbZjlZWVysjIcPpopsViUXR0tPr27evUfuBcVqtVO3bs0MmTJ5WTk6PZs2c3Wt9JSUmSLu4VEBgYqA4dOlTZv+Evf/lLo9UCAAAAwPPUecr9pQ3qbrvttirHrVarTCZTnaeol5SUaO/evbbnBQUFys/PV1BQkCIjI5WWlqYJEyaoT58+6tevnxYvXqzS0lJNnDixrqXXSWpqqlJTU227C6J+YmJi9OOPP0q6uD/BggULnNaXyWTS1q1bdfPNN0uSdu7cqWeffVYbNmxwWp9X079/f+Xk5CgqKsp2rEuXLtq9e7ch9QAAAADwLHUO9F988UWNr9W2RvhKcnNzNXjwYNvzSxvSTZgwQatWrdLYsWN19OhRzZ49W4WFhYqLi9PGjRurbZQH13TdddfZAv38+fOdGuglaeDAgbblBz179nRqX5e78cYbtWfPnirLUYKCgqrV8I9//EMBAQF69NFHG602AAAAAJ6pzoH+1ltvrfL89OnTWrNmjV5//XXl5eVp2rRpdWpv0KBBta7/njZtWp3bbSh32xTPVdfQL1++XC1bttTjjz/eqP1+9tlnjdrf2rVrde2118pisdj+rl7pC4XQ0FC9/fbbjVobAAAAAM9U5zX0l2RlZWnChAkKCwvTwoULNWTIEO3YscORtRnK3TbFc1VhYWFas2aNBg4c2Kj9Xu3WhI4wbtw4FRQUSLr4pdS1114rydhb4AEAAABoWuo0Ql9YWKhVq1bpjTfeUHFxscaMGaOysjKtX79e0dHRzqoRcCkPPvigli9fLj8/P507d04tWrSwvdanTx8DKwMAAADQlNg9Qj9y5Eh17dpVP/zwgxYvXqxDhw7p1VdfdWZthmKXe/c1fvx4p7b/2muvyc/PT5Lk7e0tk8lke+3aa6/V3r17G+3WjgAAAACaLrtH6Dds2KAZM2boscceU5cuXZxZk0twt13uXXUNvRHeeecdp7S7YcMGdezYUf7+/lc9r1OnTk7pHwAAAAAuZ/cI/VdffaXTp08rPj5eCQkJWrp0KaOQcCpX+yIlMTFRXbt2NboMAAAAAJBUh0Dfv39/rVixQocPH9Yjjzyi//mf/1F4eLgqKyu1ZcsWnT592pl1ogkaOXKk0SVU0axZnW8KAQAAAABOU+dd7v38/DRp0iR99dVX2rlzp5566in993//t9q1a6c777zTGTUagjX0uGTNmjXavXu30WUAAAAAQBX1vm2dJHXt2lV///vf9fvvv2vNmjWOqskluNtt61xtDX1MTMxVXw8MDGycQhpo6tSpuu+++9StWzejSwEAAACAKhoU6C8xm80aPXq0Pv74Y0c0Bzf3/vvvKzY29qrnrF69utZ2XOFLikmTJhldAgAAAABckUMCPXC5pKSkWs9p3rx5ree4wgZ0rJsHAAAA4KoI9DVgDb3xYmJiDJn1kZ+fr+nTp2v06NHq3bt3o/cPAAAAAPZg+LEG7nYfendj73R6I3a6j42N1ZIlSxq9XwAAAACoC0boPYQrrDd3tF69ehldAgAAAAC4LEbo4XIOHz6sP//8U1FRUUaXAgAAAAAui0APlxMaGqrQ0FCjywAAAAAAl8aUe+Ayf/vb34wuAQAAAADsQqCvgbvtcu9ua+hNJpPRJVwRMwMAAAAAuAsCfQ1SU1O1a9cu5eTkGF0KGpG7fTECAAAAoOki0MPh7Bl9JzgDAAAAQMMQ6AEAAAAAcEMEeg/BiDcAAAAANC0EeuAyfDECAAAAwF0Q6D3ElClTjC7BIxDoAQAAALgLAr2HuPnmm3Xw4EGtW7fO6FLs4qq3rQMAAAAAd0Gg9yDt27dXs2bNjC7DrTFCDwAAAMBdEOhrYLFYFB0drb59+xpdCgAAAAAA1RDoa5Camqpdu3YpJyfH6FJcWseOHev1PkbCAQAAAKBhCPSo1ZYtW2p8zcvLs/4K8UUDAAAAAHfhWWkMDhUcHKwzZ84oLCysTu9jwzsAAAAAcD4CvYdx5Aiz2WyWr6/vVc/xtPDOCD0AAAAAd0GgR4PUNwC76hcB0dHRRpcAAAAAAHbhHmeApNzcXO3cuVPDhg0zuhQAAAAAsAuBvokKCwvT4cOHjS7DJSQnJys+Pl7x8fFGlwIAAAAAdvP4Kfe//PKL4uLibA9fX1+tX7/e6LIM17NnT4e0c6Wp8+62Dj09Pd3oEgAAAACgzjx+hL5r167Kz8+XJJWUlCgqKkpDhw41tigncpcw7Up1+vv7G10CAAAAANSZx4/QX+7jjz/WbbfdJj8/P6NLAQAAAACgQQwP9FlZWRo5cqTCw8NlMpmuOB3eYrEoKipKPj4+SkhIUHZ2dr36Wrt2rcaOHdvAil2bI3eP9/b2dlhbAAAAAADHMjzQl5aWKjY2VhaL5Yqvp6enKy0tTXPmzNG3336r2NhYJSUl6ciRI7Zz4uLiFBMTU+1x6NAh2znFxcXatm2bbr/9dqdfk6d4//336/U+e6bTu+pt6wAAAADAXRi+hj45OVnJyck1vr5o0SJNmTJFEydOlCQtX75cn332mVauXKmZM2dKkm2N/NV89NFHGjZsmHx8fK56XllZmcrKymzPi4uL7bgK92NPoO7Tp08jVAIAAAAAqA/DR+ivpry8XHl5eUpMTLQd8/LyUmJiorZv316ntuydbr9gwQIFBATYHhEREXWu2x04c1O6xhh9b926tdP7AAAAAABX5tKB/tixY6qoqFBISEiV4yEhISosLLS7nVOnTik7O1tJSUm1njtr1iydOnXK9jh48GCd6zaSK+0e70wpKSlGlwAAAAAAhjJ8yn1jCAgIUFFRkV3nent7y9vbWxaLRRaLRRUVFU6uzhjOHEW350uFpvLFAwAAAAA4i0uP0AcHB8tsNlcL40VFRQoNDXVq36mpqdq1a5dycnKc2o87CAsLM7oEAAAAAMD/4dKBvkWLFoqPj1dGRobtWGVlpTIyMjRgwACn9m2xWBQdHa2+ffs6tR93EBQUpNzcXP34449GlwIAAAAA+H8Mn3JfUlKivXv32p4XFBQoPz9fQUFBioyMVFpamiZMmKA+ffqoX79+Wrx4sUpLS2273jtLamqqUlNTVVxcrICAAKf25Q7i4+MlXbzrQF5enlavXt2g9oy8bd2DDz6on3/+Wf7+/nr55ZcNqwMAAAAAGsLwQJ+bm6vBgwfbnqelpUmSJkyYoFWrVmns2LE6evSoZs+ercLCQsXFxWnjxo3VNsrDRc5em/7kk09Kki3Qu+P95AMCAlhKAQAAAMDtGR7oBw0aVGsInTZtmqZNm9ZIFV3k6ZviOcqVfneO/lJh7ty5ys/P1/r16x3aLgAAAAC4M8MDvaty1yn39o6Yu9PI+pw5cyQ5rmZ22AcAAADgCVx6UzwAAAAAAHBlBPoasMu9/T7//HO98sortuf2jKQzSg4AAAAADUOgr4Gn3Yc+ISFBP/zwg1PaHjx4sEaNGmV77uphffr06UaXAAAAAAANRqD3MDWF6ccee0w9evRweH+XRuPrur7dqDX8y5YtU7du3QzpGwAAAAAciUDfBKxcuVIPPfSQ0WW4hKlTpxpdAgAAAAA4BIG+Bp60hn7ixIny8qrbr/qPP/5wUjXGquvPAQAAAABcFemmBp62hr6uwsPD6/1eV19DDwAAAACegEDfRDl6Dbs73dceAAAAADwBgR5Nws0336wNGzYYXQYAAAAAOAyBvonq3bu37c8+Pj4GVtI41q5dq+HDhxtdBgAAAAA4DIG+Bp60Kd7lvv/+e82bN09//etf9dVXXykhIUFZWVkNbtfPz8/252bNmjW4PQAAAADA1ZG8apCamqrU1FQVFxcrICDA6HIcpmfPnurZs6ck6aabbtKOHTskSYMGDVJmZma92w0ODtaKFSvUokUL+fr6OqLUWm3ZskVDhw5tlL4AAAAAwNUQ6CFJSk9P1wMPPKCMjIx6t/Hwww87sKLaJSYmNmp/AAAAAOBKmHLvYep7y7h27drp+eefd3A1AAAAAABnIdCjQdzldnWhoaFGlwAAAAAADkWgR4PUd0ZAY3OXLx4AAAAAwF4E+hp46i73V3P99dc3Wl9ms7lB73/qqaccVAkAAAAAuCc2xauBp+5yfzVhYWHKz89X69atndbH9OnTtWvXLt166631bmPAgAGKjIx0YFUAAAAA4H4I9KgiNjbWqe0vWbKkwW1wn3sAAAAAYMo9AAAAAABuiUCPBjFiszk2uAMAAAAAAj0AAAAAAG6JxcjwaNddd52mTp1qdBkAAAAA4HAEejSIEfehr0ufn3zyiaKjo51YDQAAAAAYgyn3NXDX+9AbEbABAAAAAI2PQF+D1NRU7dq1Szk5OUaXAgAAAABANQR6NIir73LPjvgAAAAAPBWBHh7Ny4u/4gAAAAA8E2kHHu366683ugQAAAAAcAoCvYdhU7yqmHIPAAAAwFMR6OF2unXrZnQJAAAAAGA47kPvYTx5RHr79u1avXq1/uu//svoUgAAAADAcE1ihP6VV15R9+7dFR0drRkzZjAt3U31799fr776qgIDA23HDh06pK+//tq4ogAAAADAIB4/Qn/06FEtXbpUP/30k5o3b65bbrlFO3bs0IABA4wuDQ4QFhamsLAwo8sAAAAAgEbn8YFeki5cuKBz585Jks6fP6927doZXBEAAAAAAA1j+JT7rKwsjRw5UuHh4TKZTFq/fn21cywWi6KiouTj46OEhARlZ2fb3f4111yjp59+WpGRkQoPD1diYqI6derkwCtwLSwnAAAAAICmwfBAX1paqtjYWFksliu+np6errS0NM2ZM0fffvutYmNjlZSUpCNHjtjOiYuLU0xMTLXHoUOHdOLECX366afav3+//vjjD23btk1ZWVmNdXkAAAAAADiF4VPuk5OTlZycXOPrixYt0pQpUzRx4kRJ0vLly/XZZ59p5cqVmjlzpiQpPz+/xvevW7dOnTt3VlBQkCRpxIgR2rFjh2655ZYrnl9WVqaysjLb8+Li4rpeUpPiybvqAwAAAIArM3yE/mrKy8uVl5enxMRE2zEvLy8lJiZq+/btdrURERGhbdu26dy5c6qoqFBmZqa6du1a4/kLFixQQECA7REREdHg6wAAAAAAwNFcOtAfO3ZMFRUVCgkJqXI8JCREhYWFdrXRv39/3X777erVq5d69uypTp066c4776zx/FmzZunUqVO2x8GDBxt0DZ6ONfsAAAAAYAzDp9w3hhdeeEEvvPCCXed6e3vL29tbFotFFotFFRUVTq4OAAAAAIC6c+kR+uDgYJnNZhUVFVU5XlRUpNDQUKf2nZqaql27diknJ8ep/QAAAAAAUB8uHehbtGih+Ph4ZWRk2I5VVlYqIyNDAwYMMLAyuKqBAwcaXQIAAAAANArDp9yXlJRo7969tucFBQXKz89XUFCQIiMjlZaWpgkTJqhPnz7q16+fFi9erNLSUtuu987ClHv7uNou965WDwAAAAA4i+GBPjc3V4MHD7Y9T0tLkyRNmDBBq1at0tixY3X06FHNnj1bhYWFiouL08aNG6ttlOdoqampSk1NVXFxsQICApzaFwAAAAAAdWV4oB80aFCtO6VPmzZN06ZNa6SK4M4YoQcAAADQVLj0GnojWSwWRUdHq2/fvkaXUidN/TZyBHoAAAAATQWBvgbscm8fV/sCwWQyKTAwUJLUrJnhE1AAAAAAwGkI9PAoJpNJmZmZGjZsmHbs2GF0OQAAAADgNAxh1oBd7u3jilPcY2NjtWnTJqPLAAAAAACnYoS+Bky5d0+u+AUDAAAAADgDgd7DdO/e3egSDEWgBwAAANBUEOg9TExMjBISEowuwzAEegAAAABNBYG+Bu562zpJGjp0qNElGIZADwAAAKCpINDXgDX09nG1AO1q9QAAAACAsxDo0SCudh/6gQMHGl0CAAAAADQKblsHj/Drr79q8+bNmjx5stGlAAAAAECjINDXwJ3vQ+9qo+aNoXPnzurcubPRZQAAAABAo2HKfQ1YQw8AAAAAcGUEeg+3aNEio0sAAAAAADgBgd7DPfnkk05tn13lAQAAAMAYBHoAAAAAANwQgR4AAAAAADdEoK+BxWJRdHS0+vbta3QpddaY0+Cb4o76AAAAAOAKCPQ1YJd7AAAAAIArI9ADAAAAAOCGCPRoEHa5BwAAAABjEOgBAAAAAHBDBHoAAAAAANwQgR4AAAAAADdEoPdA3EoOAAAAADwfgb4G7nwf+sZw4403SpImT55scCUAAAAA0DQ1M7oAV5WamqrU1FQVFxcrICDA6HJczubNm5WXl6ebbrrJ6FIAAAAAoEki0KNe/Pz8dMsttxhdBgAAAAA0WUy5BwAAAADADRHoPZDJZDK6BAAAAACAkxHoAQAAAABwQwR6AAAAAADcEIEeAAAAAAA3RKAHAAAAAMANNYlAv3DhQnXv3l0xMTF69913jS4HAAAAAIAG8/j70O/cuVPvvfee8vLyZLVaNXjwYN1xxx0KDAw0ujQAAAAAAOrN40fod+/erQEDBsjHx0e+vr6KjY3Vxo0bjS4LAAAAAIAGMTzQZ2VlaeTIkQoPD5fJZNL69eurnWOxWBQVFSUfHx8lJCQoOzvb7vZjYmKUmZmpkydP6sSJE8rMzNQff/zhwCsAAAAAAKDxGT7lvrS0VLGxsZo0aZLuuuuuaq+np6crLS1Ny5cvV0JCghYvXqykpCT98ssvateunSQpLi5OFy5cqPbezZs3Kzo6WjNmzNCQIUMUEBCg/v37y2w2O/26jGS1Wo0uAQAAAADgZIYH+uTkZCUnJ9f4+qJFizRlyhRNnDhRkrR8+XJ99tlnWrlypWbOnClJys/Pv2ofjzzyiB555BFJ0sMPP6wuXbrUeG5ZWZnKyspsz4uLi+29FAAAAAAAGo3hU+6vpry8XHl5eUpMTLQd8/LyUmJiorZv3253O0eOHJEk/fLLL8rOzlZSUlKN5y5YsEABAQG2R0RERP0vAAAAAAAAJzF8hP5qjh07poqKCoWEhFQ5HhISop9//tnudkaNGqVTp07Jz89Pb775ppo1q/myZ82apbS0NNvz4uJitwv1JpPJ6BIAAAAAAE7m0oHeUeoymu/t7S1vb29ZLBZZLBZVVFQ4sTIAAAAAAOrHpafcBwcHy2w2q6ioqMrxoqIihYaGOrXv1NRU7dq1Szk5OU7tBwAAAACA+nDpQN+iRQvFx8crIyPDdqyyslIZGRkaMGCAU/u2WCyKjo5W3759ndoPAAAAAAD1YfiU+5KSEu3du9f2vKCgQPn5+QoKClJkZKTS0tI0YcIE9enTR/369dPixYtVWlpq2/XeWVJTU5Wamqri4mIFBAQ4tS8AAAAAAOrK8ECfm5urwYMH255f2pBuwoQJWrVqlcaOHaujR49q9uzZKiwsVFxcnDZu3FhtozwAAAAAAJoSwwP9oEGDZLVar3rOtGnTNG3atEaq6CI2xQMAAAAAuDKXXkNvJDbFAwAAAAC4MgI9AAAAAABuiEBfA3a5BwAAAAC4MgJ9DZhyDwAAAABwZQR6D3T33XdLkjp06GBwJQAAAAAAZzF8l3s4XlxcnAoKCri1HwAAAAB4MEboa+Dua+ijoqLk6+trdBkAAAAAACcxWWu7CXwTV1xcrICAAJ06dUqtW7c2uhwAAAAAgIezN4cyQg8AAAAAgBsi0AMAAAAA4IYI9AAAAAAAuCECfQ3cfVM8AAAAAIBnY1O8WrApHgAAAACgMbEpHgAAAAAAHoxADwAAAACAGyLQAwAAAADghgj0NWBTPAAAAACAK2NTvFqwKR4AAAAAoDGxKR4AAAAAAB6smdEFuLpLExiKi4sNrgQAAAAA0BRcyp+1Tagn0Nfi9OnTkqSIiAiDKwEAAAAANCWnT59WQEBAja+zhr4WlZWVOnTokPz9/WUymYwu56r69u2rnJwco8swjLtev6vVbUQ9zu7TGe07ss2GtlVcXKyIiAgdPHiQvT7cjKv9+29s7nr9rlS3J35mO6sPV/nc5jPbvbnSv38juOv1u1Ld9tZitVp1+vRphYeHy8ur5pXyjNDXwsvLS+3btze6DLuYzeYm/R8Gd71+V6vbiHqc3acz2ndkm45qq3Xr1i71dwm1c7V//43NXa/fler2xM9sZ/Xhap/bfGa7J1f6928Ed71+V6q7LrVcbWT+EjbF8yCpqalGl2Aod71+V6vbiHqc3acz2ndkm672dwCNp6n/7t31+l2pbk/8zHZWH3xuwxGa+u/eXa/flep2dC1MuQcAN8ftNQHAffCZDcCRGKEHADfn7e2tOXPmyNvb2+hSAAC14DMbgCMxQg8AAAAAgBtihB4AAAAAADdEoAcAAAAAwA0R6AEAAAAAcEMEegAAAAAA3BCBHgAAAAAAN0SgBwAPdfDgQQ0aNEjR0dHq2bOn1q1bZ3RJAICrOHnypPr06aO4uDjFxMRoxYoVRpcEwMVx2zoA8FCHDx9WUVGR4uLiVFhYqPj4eO3Zs0d+fn5GlwYAuIKKigqVlZWpZcuWKi0tVUxMjHJzc9W2bVujSwPgopoZXQAAwDnCwsIUFhYmSQoNDVVwcLCOHz9OoAcAF2U2m9WyZUtJUllZmaxWqxh7A3A1TLkHABeVlZWlkSNHKjw8XCaTSevXr692jsViUVRUlHx8fJSQkKDs7OwrtpWXl6eKigpFREQ4uWoAaLoc8bl98uRJxcbGqn379nrmmWcUHBzcSNUDcEcEegBwUaWlpYqNjZXFYrni6+np6UpLS9OcOXP07bffKjY2VklJSTpy5EiV844fP67x48frn//8Z2OUDQBNliM+twMDA/X999+roKBA7733noqKihqrfABuiDX0AOAGTCaTPvzwQ40ePdp2LCEhQX379tXSpUslSZWVlYqIiND06dM1c+ZMSRenbA4dOlRTpkzRQw89ZETpANAk1fdz+3KPP/64hgwZonvuuaexygbgZhihBwA3VF5erry8PCUmJtqOeXl5KTExUdu3b5ckWa1WpaSkaMiQIYR5ADCYPZ/bRUVFOn36tCTp1KlTysrKUteuXQ2pF4B7INADgBs6duyYKioqFBISUuV4SEiICgsLJUlff/210tPTtX79esXFxSkuLk47d+40olwAaPLs+dw+cOCABg4cqNjYWA0cOFDTp09Xjx49jCgXgJtgl3sA8FA333yzKisrjS4DAGCnfv36KT8/3+gyALgRRugBwA0FBwfLbDZX2yypqKhIoaGhBlUFAKgJn9sAnIFADwBuqEWLFoqPj1dGRobtWGVlpTIyMjRgwAADKwMAXAmf2wCcgSn3AOCiSkpKtHfvXtvzgoIC5efnKygoSJGRkUpLS9OECRPUp08f9evXT4sXL1ZpaakmTpxoYNUA0HTxuQ2gsXHbOgBwUZmZmRo8eHC14xMmTNCqVaskSUuXLtVLL72kwsJCxcXFacmSJUpISGjkSgEAEp/bABofgR4AAAAAADfEGnoAAAAAANwQgR4AAAAAADdEoAcAAAAAwA0R6AEAAAAAcEMEegAAAAAA3BCBHgAAAAAAN0SgBwAAAADADRHoAQAAAABwQwR6AADg1srLy9W5c2dt27bNoe1u3LhRcXFxqqysdGi7AAA4CoEeAAAXkpKSIpPJVO2xd+9eo0tzWcuXL1fHjh1144032o6ZTCatX7++2rkpKSkaPXq0Xe0OHz5czZs31+rVqx1UKQAAjkWgBwDAxQwfPlyHDx+u8ujYsWO188rLyw2ozrVYrVYtXbpUkydPdkr7KSkpWrJkiVPaBgCgoQj0AAC4GG9vb4WGhlZ5mM1mDRo0SNOmTdMTTzyh4OBgJSUlSZJ+/PFHJScnq1WrVgoJCdFDDz2kY8eO2dorLS3V+PHj1apVK4WFhenll1/WoEGD9MQTT9jOudKIdmBgoFatWmV7fvDgQY0ZM0aBgYEKCgrSqFGjtH//ftvrl0a/Fy5cqLCwMLVt21apqak6f/687ZyysjI9++yzioiIkLe3tzp37qw33nhDVqtVnTt31sKFC6vUkJ+ff9UZCnl5edq3b59GjBhRx5+ytH///ivOhhg0aJDtnJEjRyo3N1f79u2rc/sAADgbgR4AADfy1ltvqUWLFvr666+1fPlynTx5UkOGDFGvXr2Um5urjRs3qqioSGPGjLG955lnntGXX36pjz76SJs3b1ZmZqa+/fbbOvV7/vx5JSUlyd/fX1u3btXXX3+tVq1aafjw4VVmCnzxxRfat2+fvvjiC7311ltatWpVlS8Fxo8frzVr1mjJkiXavXu3/vGPf6hVq1YymUyaNGmS3nzzzSr9vvnmm7rlllvUuXPnK9a1detWXX/99fL396/T9UhSRERElVkQ3333ndq2batbbrnFdk5kZKRCQkK0devWOrcPAICzNTO6AAAAUNWnn36qVq1a2Z4nJydr3bp1kqQuXbro73//u+21efPmqVevXpo/f77t2MqVKxUREaE9e/YoPDxcb7zxht59913ddtttki5+KdC+ffs61ZSenq7Kykq9/vrrMplMki6G7cDAQGVmZmrYsGGSpDZt2mjp0qUym83q1q2bRowYoYyMDE2ZMkV79uzR2rVrtWXLFiUmJkqSrrvuOlsfKSkpmj17trKzs9WvXz+dP39e7733XrVR+8sdOHBA4eHhV3zt/vvvl9lsrnKsrKzMNppvNpsVGhoqSTp37pxGjx6tAQMGaO7cuVXeEx4ergMHDtThpwUAQOMg0AMA4GIGDx6sZcuW2Z77+fnZ/hwfH1/l3O+//15ffPFFlS8ALtm3b5/Onj2r8vJyJSQk2I4HBQWpa9eudarp+++/1969e6uNhJ87d67KdPTu3btXCdFhYWHauXOnpIvT581ms2699dYr9hEeHq4RI0Zo5cqV6tevnz755BOVlZXp3nvvrbGus2fPysfH54qvvfLKK7YvDi559tlnVVFRUe3cSZMm6fTp09qyZYu8vKpOYPT19dWZM2dqrAEAAKMQ6AEAcDF+fn41TjG/PNxLUklJiUaOHKkXX3yx2rlhYWF2745vMplktVqrHLt87XtJSYni4+OvuOP7NddcY/tz8+bNq7V76bZvvr6+tdbx8MMP66GHHtIrr7yiN998U2PHjlXLli1rPD84ONj2hcH/FRoaWu3n6O/vr5MnT1Y5Nm/ePG3atEnZ2dlXnLp//PjxKtcIAICrINADAODGevfurffff19RUVFq1qz6f9Y7deqk5s2b65tvvlFkZKQk6cSJE9qzZ0+VkfJrrrlGhw8ftj3/9ddfq4xK9+7dW+np6WrXrp1at25dr1p79OihyspKffnll9VGzi+5/fbb5efnp2XLlmnjxo3Kysq6apu9evXSsmXLZLVabUsB6uL999/Xf/7nf2rDhg3q1KlTtdcvzUDo1atXndsGAMDZ2BQPAAA3lpqaquPHj+v+++9XTk6O9u3bp02bNmnixImqqKhQq1atNHnyZD3zzDP6/PPP9eOPPyolJaXatPIhQ4Zo6dKl+u6775Sbm6tHH320ymj7uHHjFBwcrFGjRmnr1q0qKChQZmamZsyYod9//92uWqOiojRhwgRNmjRJ69evt7Wxdu1a2zlms1kpKSmaNWuWunTpogEDBly1zcGDB6ukpEQ//fRTHX5qF/34448aP368nn32WXXv3l2FhYUqLCzU8ePHbefs2LFD3t7etdYBAIARCPQAALix8PBwff3116qoqNCwYcPUo0cPPfHEEwoMDLSF9pdeekkDBw7UyJEjlZiYqJtvvrnaWvyXX35ZERERGjhwoB544AE9/fTTVaa6t2zZUllZWYqMjNRdd92lG264QZMnT9a5c+fqNGK/bNky3XPPPXr88cfVrVs3TZkyRaWlpVXOmTx5ssrLyzVx4sRa22vbtq3+7d/+7YpLAWqTm5urM2fOaN68eQoLC7M97rrrLts5a9as0bhx46467R8AAKOYrP93wRwAAPB4gwYNUlxcnBYvXmx0KdVs3bpVt912mw4ePKiQkJBaz//hhx80dOhQ7du374qbA9bXsWPH1LVrV+Xm5qpjx44OaxcAAEdhhB4AALiEsrIy/f7775o7d67uvfdeu8K8JPXs2VMvvviiCgoKHFrP/v379dprrxHmAQAui03xAACAS1izZo0mT56suLg4vf3223V6b0pKisPr6dOnj/r06ePwdgEAcBSm3AMAAAAA4IaYcg8AAAAAgBsi0AMAAAAA4IYI9AAAAAAAuCECPQAAAAAAbohADwAAAACAGyLQAwAAAADghgj0AAAAAAC4IQI9AAAAAABuiEAPAAAAAIAb+v8Ap95gCYi9POcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", + "plt.subplot(2, 1, 1)\n", + "\n", + "# Plot the amplitude\n", + "diffs_amplitude = abs(A_lalsuite - A_ripple) / A_lalsuite\n", + "plt.plot(f, diffs_amplitude, \"-\", color=\"black\")\n", + "\n", + "plt.title(\n", + " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "# plt.ylim(1e-27, 1e-21)\n", + "# plt.legend()\n", + "\n", + "# Plot the angle or the phase\n", + "plt.subplot(2, 1, 2)\n", + "\n", + "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\")\n", + "name = \"Angle\"\n", + "# plt.legend()\n", + "plt.title(\n", + " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0539dfc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jim_dev_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f72f42a719356be65957f84aea89b2ec20cc9440 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Wed, 29 Oct 2025 14:23:48 +0100 Subject: [PATCH 022/474] Testing NRTidalv3. Looks like some phase term is missing... --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 8 +- src/ripplegw/waveforms/NRTidalv3_utils.py | 106 +-- test/NRTidalv3_testing.ipynb | 806 ++++++++++++++++++ 3 files changed, 864 insertions(+), 56 deletions(-) create mode 100644 test/NRTidalv3_testing.ipynb diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index c87e677b..ac7eb972 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -5,11 +5,13 @@ from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C from ..typing import Array from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas -from .IMRPhenom_tidal_utils import get_quadparam_octparam, get_kappa -from .IMRPhenomD_NRTidalv2 import get_tidal_amplitude, get_spin_phase_correction, get_planck_taper # Same between v2 and v3 +from .IMRPhenom_tidal_utils import get_kappa +from .IMRPhenomD_NRTidalv2 import get_spin_phase_correction, get_planck_taper, get_tidal_amplitude # Same between v2 and v3 from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper from ripplegw.waveforms import IMRPhenomX_utils from .IMRPhenomXAS import Amp, Phase +import lalsimulation as lalsim +import lal # This could be a general utils function @@ -72,7 +74,7 @@ def _gen_IMRPhenomXAS_NRTidalv3( psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) - h0 = AA_P * (bbh_amp + A_T) * jnp.exp(1.0j * -(bbh_psi + psi_T + psi_SS)) + h0 = AA_P * (bbh_amp + A_T) * jnp.exp(1.0j * (bbh_psi + psi_T + psi_SS)) return h0 diff --git a/src/ripplegw/waveforms/NRTidalv3_utils.py b/src/ripplegw/waveforms/NRTidalv3_utils.py index 35f5e5f7..22453f85 100644 --- a/src/ripplegw/waveforms/NRTidalv3_utils.py +++ b/src/ripplegw/waveforms/NRTidalv3_utils.py @@ -202,6 +202,55 @@ def get_tidal_phase( return tidal_phase +""" +PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, +see Eq. (22) and (45) of https://arxiv.org/pdf/2311.07456.pdf +and is a function of x = angular_orb_freq^(2./3.) +""" +def get_tidal_phase_PN( + M_omega, # Dimensionless angular GW frequency + Xa, #< Mass of companion 1 divided by total mass + lambda1, #< dimensionless tidal deformability of companion 1 + lambda2, #< dimensionless tidal deformability of companion 2 + PN_coeffs #< 7.5 PN coefficients +): + """ + SimNRTunedTidesFDTidalPhase_PN + """ + Xb = 1.0 - Xa + + PN_x = M_omega **(2.0/3.0) # pow(M_omega, 2.0/3.0) + PN_x_2 = PN_x * PN_x # pow(PN_x, 2) + PN_x_3over2 = PN_x * jnp.sqrt(PN_x) # pow(PN_x, 3.0/2.0) + PN_x_5over2 = PN_x_3over2 * PN_x # pow(PN_x, 5.0/2.0) + + kappaA = 3.0*Xb*Xa*Xa*Xa*Xa*lambda1 + kappaB = 3.0*Xa*Xb*Xb*Xb*Xb*lambda2 + + # 7.5PN Coefficients + c_NewtA = PN_coeffs[0] + c_1A = PN_coeffs[1] + c_3over2A = PN_coeffs[2] + c_2A = PN_coeffs[3] + c_5over2A = PN_coeffs[4] + + c_NewtB = PN_coeffs[5] + c_1B = PN_coeffs[6] + c_3over2B = PN_coeffs[7] + c_2B = PN_coeffs[8] + c_5over2B = PN_coeffs[9] + + factorA = -c_NewtA*PN_x_5over2*kappaA + factorB = -c_NewtB*PN_x_5over2*kappaB + + tidal_phasePNA = factorA*(1.0 + (c_1A*PN_x) + (c_3over2A*PN_x_3over2) + (c_2A*PN_x_2) + (c_5over2A*PN_x_5over2)) + tidal_phasePNB = factorB*(1.0 + (c_1B*PN_x) + (c_3over2B*PN_x_3over2) + (c_2B*PN_x_2) + (c_5over2B*PN_x_5over2)) + + tidal_phasePN = tidal_phasePNA + tidal_phasePNB + + return tidal_phasePN + + def get_tidalphasePN_coeffs(theta_intrinsic: Array): """ Coefficients or the PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, see Eq. (45) of https://arxiv.org/pdf/2311.07456.pdf @@ -295,11 +344,11 @@ def get_NRTidalv3_coefficients( alpha = -8.08155404e-03 #alpha beta = -1.13695919e+00 #beta - kappaA_alpha = jnp.pow(NRTidalv3_coeffs[4] + 1, alpha) #kappaA_alpha - kappaB_alpha = jnp.pow(NRTidalv3_coeffs[5] + 1, alpha) #kappaB_alpha + kappaA_alpha = (NRTidalv3_coeffs[4] + 1) ** alpha #kappaA_alpha + kappaB_alpha = (NRTidalv3_coeffs[5] + 1) ** alpha #kappaB_alpha - Xa_beta = jnp.pow(Xa, beta) #Xa_beta - Xb_beta = jnp.pow(Xb, beta) #Xb_beta + Xa_beta = (Xa) ** beta #Xa_beta + Xb_beta = (Xb) ** beta #Xb_beta # Pade approximant coefficients: n_5over20 = -9.40654388e+02 #n_5over20 @@ -343,55 +392,6 @@ def get_NRTidalv3_coefficients( return NRTidalv3_coeffs -""" -PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, -see Eq. (22) and (45) of https://arxiv.org/pdf/2311.07456.pdf -and is a function of x = angular_orb_freq^(2./3.) -""" -def get_tidal_phase_PN( - M_omega, # Dimensionless angular GW frequency - Xa, #< Mass of companion 1 divided by total mass - lambda1, #< dimensionless tidal deformability of companion 1 - lambda2, #< dimensionless tidal deformability of companion 2 - PN_coeffs #< 7.5 PN coefficients -): - """ - SimNRTunedTidesFDTidalPhase_PN - """ - Xb = 1.0 - Xa - - PN_x = M_omega **(2.0/3.0) # pow(M_omega, 2.0/3.0) - PN_x_2 = PN_x * PN_x # pow(PN_x, 2) - PN_x_3over2 = PN_x * jnp.sqrt(PN_x) # pow(PN_x, 3.0/2.0) - PN_x_5over2 = PN_x_3over2 * PN_x # pow(PN_x, 5.0/2.0) - - kappaA = 3.0*Xb*Xa*Xa*Xa*Xa*lambda1 - kappaB = 3.0*Xa*Xb*Xb*Xb*Xb*lambda2 - - # 7.5PN Coefficients - c_NewtA = PN_coeffs[0] - c_1A = PN_coeffs[1] - c_3over2A = PN_coeffs[2] - c_2A = PN_coeffs[3] - c_5over2A = PN_coeffs[4] - - c_NewtB = PN_coeffs[5] - c_1B = PN_coeffs[6] - c_3over2B = PN_coeffs[7] - c_2B = PN_coeffs[8] - c_5over2B = PN_coeffs[9] - - factorA = -c_NewtA*PN_x_5over2*kappaA - factorB = -c_NewtB*PN_x_5over2*kappaB - - tidal_phasePNA = factorA*(1.0 + (c_1A*PN_x) + (c_3over2A*PN_x_3over2) + (c_2A*PN_x_2) + (c_5over2A*PN_x_5over2)) - tidal_phasePNB = factorB*(1.0 + (c_1B*PN_x) + (c_3over2B*PN_x_3over2) + (c_2B*PN_x_2) + (c_5over2B*PN_x_5over2)) - - tidal_phasePN = tidal_phasePNA + tidal_phasePNB - - return tidal_phasePN - - ###################################################################################################################################### ######################################################### SPIN EFFECTS ######################################################### ###################################################################################################################################### diff --git a/test/NRTidalv3_testing.ipynb b/test/NRTidalv3_testing.ipynb new file mode 100644 index 00000000..d49f341c --- /dev/null +++ b/test/NRTidalv3_testing.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "22705cfa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(False)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from ripplegw.constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C\n", + "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc, gen_IMRPhenomXAS_NRTidalv3\n", + "from ripplegw import ms_to_Mc_eta, lambdas_to_lambda_tildes, get_eff_pads, get_match_arr\n", + "\n", + "import lalsimulation as lalsim\n", + "import lal\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7bd3da0b", + "metadata": {}, + "outputs": [], + "source": [ + "# Frequency grid\n", + "T = 128\n", + "f_l = 20.0\n", + "f_sampling = 2 * 4096\n", + "f_u = f_sampling // 2\n", + "f_ref = f_l\n", + "\n", + "delta_t = 1 / f_sampling\n", + "tlen = int(round(T / delta_t))\n", + "freqs = np.fft.rfftfreq(tlen, delta_t)\n", + "df = freqs[1] - freqs[0]\n", + "fs = freqs[(freqs > f_l) & (freqs < f_u)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f33f2db", + "metadata": {}, + "outputs": [], + "source": [ + "m1 = 1.37 # In solar masses\n", + "m2 = 1.37\n", + "chi1 = 0 #0.141 # Dimensionless spin\n", + "chi2 = 0 #0.141\n", + "lambda1 = 1001.8\n", + "lambda2 = 1001.8\n", + "tc = 0.0 # Time of coalescence in seconds\n", + "phic = 0.0 # Time of coalescence\n", + "dist_mpc = 440 # Distance to source in Mpc\n", + "inclination = 0.0 # Inclination Angle\n", + "\n", + "q = m1/m2\n", + "Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", + "lambda_tilde, delta_lambda_tilde = lambdas_to_lambda_tildes(\n", + " jnp.array([lambda1, lambda2, m1, m2])\n", + ")\n", + "\n", + "theta_ripple = jnp.array(\n", + " [\n", + " Mc,\n", + " eta,\n", + " chi1,\n", + " chi2,\n", + " lambda_tilde,\n", + " delta_lambda_tilde,\n", + " dist_mpc,\n", + " tc,\n", + " phic,\n", + " inclination,\n", + " ]\n", + ")\n", + "fs_ripple = jnp.arange(f_l, f_u, df)[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a330c909", + "metadata": {}, + "outputs": [], + "source": [ + "from ripplegw.waveforms.NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper\n", + "from ripplegw.waveforms.IMRPhenomD_NRTidalv2 import get_planck_taper, get_tidal_amplitude, get_spin_phase_correction\n", + "from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc\n", + "from ripplegw.waveforms.IMRPhenom_tidal_utils import get_kappa" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a65d3d67", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1595.8424\n" + ] + } + ], + "source": [ + "theta_intrinsic = jnp.array([m1, m2, chi1, chi2, lambda1, lambda2])\n", + "M_s = m1 + m2\n", + "Xa = m1/M_s\n", + "f_merger_ripple = _get_merger_frequency(theta_intrinsic)\n", + "print(f_merger_ripple)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c1dbdc6b", + "metadata": {}, + "outputs": [], + "source": [ + "PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic)\n", + "NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs)\n", + "\n", + "M_omega = (PI * M_s *gt * fs_ripple)\n", + "A_P = general_planck_taper(fs, 1.15*f_merger_ripple, 1.35*f_merger_ripple)\n", + "\n", + "kappa = get_kappa(jnp.array([m1,m2,chi1,chi2,lambda1,lambda2]))\n", + "tidal_amp = get_tidal_amplitude(M_omega**(2/3), theta_intrinsic, kappa, distance=dist_mpc)\n", + "\n", + "tidal_phase = get_tidal_phase(M_omega, NRTidalv3_coeffs, PN_coeffs)\n", + "tidal_phasePN = get_tidal_phase_PN(M_omega, Xa, lambda1, lambda2, PN_coeffs)\n", + "tidal_phase_corr = tidal_phase * (1 - A_P) + tidal_phasePN * A_P" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "610906ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False False ... False True False]\n", + "215839\n", + "True\n", + "[False False False ... True True True]\n" + ] + } + ], + "source": [ + "increasing = jnp.concatenate([jnp.array([False]), tidal_phase[1:] >= tidal_phase[:-1]])\n", + "fHzmrgcheck = 0.9*f_merger_ripple\n", + "# Apply fHz threshold\n", + "valid = (fs >= fHzmrgcheck) & increasing\n", + "print(valid)\n", + "first_valid = jnp.argmax(valid) # returns 0 if none are True\n", + "print(first_valid)\n", + "found = jnp.any(valid)\n", + "print(found)\n", + "tidal_min_value = jnp.where(found, tidal_phase[first_valid - 1], 0.0)\n", + "\n", + "# Build mask for indices after that point\n", + "mask = (jnp.arange(fs.size) > (first_valid - 1)) & found\n", + "print(mask)\n", + "phi_tidal = jnp.where(mask, tidal_min_value, tidal_phase)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f842e550", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(fs, tidal_phase,label='Phase corr')\n", + "plt.plot(fs, tidal_phasePN,label='PN corr')\n", + "plt.plot(fs, tidal_phase_corr, ls='--',label='Full corr')\n", + "plt.legend()\n", + "plt.xlabel('f')\n", + "# plt.xscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9d8657b1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tidal_phase_corr_withMIN = phi_tidal * (1 - A_P) + tidal_phasePN * A_P\n", + "plt.plot(fs, phi_tidal,label='Phase corr')\n", + "plt.plot(fs, tidal_phasePN,label='PN corr')\n", + "plt.plot(fs, tidal_phase_corr_withMIN, ls='--',label='Full corr')\n", + "plt.legend()\n", + "plt.xlabel('f')\n", + "# plt.xscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d943c569", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "laldict = lal.CreateDict()\n", + "lalsim.SimInspiralWaveformParamsInsertTidalLambda1(laldict, lambda1)\n", + "lalsim.SimInspiralWaveformParamsInsertTidalLambda2(laldict, lambda2)\n", + "quad1 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda1)\n", + "quad2 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda2)\n", + "# print(quad1)\n", + "oct1 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad1)\n", + "oct2 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad2)\n", + "# print(oct1)\n", + "lalsim.SimInspiralWaveformParamsInsertdQuadMon1(laldict, quad1 - 1)\n", + "lalsim.SimInspiralWaveformParamsInsertdQuadMon2(laldict, quad2 - 1)\n", + "\n", + "IMRphenom = \"IMRPhenomXAS_NRTidalv3\"\n", + "approximant = lalsim.SimInspiralGetApproximantFromString(IMRphenom)\n", + "\n", + "theta = np.array(\n", + " [m1, m2, chi1, chi2, lambda1, lambda2, dist_mpc, tc, phic, inclination]\n", + ")\n", + "\n", + "m1_kg = theta[0] * lal.MSUN_SI\n", + "m2_kg = theta[1] * lal.MSUN_SI\n", + "distance = dist_mpc * 1e6 * lal.PC_SI\n", + "\n", + "ph_tdLAL = np.zeros(fs.shape)\n", + "amp_tdLAL = np.zeros(fs.shape)\n", + "pl_taperLAL = np.zeros(fs.shape)\n", + "\n", + "lalsim.SimNRTunedTidesFDTidalPhaseFrequencySeries(\n", + " phi_tidal=ph_tdLAL,\n", + " amp_tidal=amp_tdLAL,\n", + " planck_taper=pl_taperLAL,\n", + " fHz=fs,\n", + " m1_SI=m1_kg,\n", + " m2_SI=m2_kg,\n", + " lambda1=lambda1,\n", + " lambda2=lambda2,\n", + " chi1=chi1,\n", + " chi2=chi2,\n", + " NRTidal_version=lalsim.NRTidalv3_V\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fcfeb8b7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 5))\n", + "delta_psi_tidal = ph_tdLAL - tidal_phase_corr\n", + "plt.plot(fs, abs(delta_psi_tidal), \"-\", color=\"black\", label='')\n", + "plt.axvline(f_merger_ripple, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger_ripple, linestyle=\"--\", color=\"black\")\n", + "# plt.legend()\n", + "plt.title(\n", + " r\"Tidal phase difference ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5, 1.2 * f_merger_ripple + 20)\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8af9d8b5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 5))\n", + "plt.plot(fs, jnp.cumsum(delta_psi_tidal), c='k')\n", + "plt.axvline(f_merger_ripple, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger_ripple, linestyle=\"--\", color=\"black\")\n", + "plt.title(\n", + " r\"Cumulative error ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5, 1.2 * f_merger_ripple + 20)\n", + "# plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "080c2e51", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4da0afb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bd08c356", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([ 5.4772518e-24-2.8787021e-24j, 1.0637312e-24+6.0926741e-24j,\n", + " -6.1303162e-24-7.9777775e-25j, ..., 0.0000000e+00+0.0000000e+00j,\n", + " 0.0000000e+00+0.0000000e+00j, 0.0000000e+00+0.0000000e+00j], dtype=complex64)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# And finally lets generate the waveform!\n", + "hp_ripple, hc_ripple = gen_IMRPhenomXAS_NRTidalv3_hphc(\n", + " fs_ripple, theta_ripple, f_ref\n", + ")\n", + "hp_ripple" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5616c838", + "metadata": {}, + "outputs": [], + "source": [ + "hp, _ = lalsim.SimInspiralChooseFDWaveform(\n", + " m1_kg,\n", + " m2_kg,\n", + " 0.0,\n", + " 0.0,\n", + " chi1,\n", + " 0.0,\n", + " 0.0,\n", + " chi2,\n", + " distance,\n", + " inclination,\n", + " phic,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " df,\n", + " f_l,\n", + " f_u,\n", + " f_ref,\n", + " laldict,\n", + " approximant,\n", + ")\n", + "\n", + "approximantXAS = lalsim.SimInspiralGetApproximantFromString(\"IMRphenomXAS\")\n", + "laldictXAS = lal.CreateDict()\n", + "hpXAS, _ = lalsim.SimInspiralChooseFDWaveform(\n", + " m1_kg,\n", + " m2_kg,\n", + " 0.0,\n", + " 0.0,\n", + " chi1,\n", + " 0.0,\n", + " 0.0,\n", + " chi2,\n", + " distance,\n", + " inclination,\n", + " phic,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " df,\n", + " f_l,\n", + " f_u,\n", + " f_ref,\n", + " laldictXAS,\n", + " approximantXAS,\n", + ")\n", + "\n", + "freqs_lal = np.arange(len(hp.data.data)) * df\n", + "\n", + "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", + "hp_lalsuite = hp.data.data[mask_lal]\n", + "hpXAS_lalsuite = hpXAS.data.data[mask_lal]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "05c1e340", + "metadata": {}, + "outputs": [], + "source": [ + "f = freqs_lal[mask_lal]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0f7b849c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = 0.75\n", + "plt.subplots(1, 1, figsize=(17, 7))\n", + "# plt.subplot(1, 2, 1)\n", + "\n", + "plt.plot(f, hp_lalsuite.real, \"-\", label=\"LAL\", alpha=a)\n", + "plt.plot(f, hp_ripple.real, \"-\", label=\"ripple\", alpha=a)\n", + "# plt.plot(fs, hpXAS_lalsuite.real, label=\"LAL XAS\", alpha=a)\n", + "plt.title(f\"h+, NRTidalv3 lambda1, lambda2 = {lambda1, lambda2}\")\n", + "plt.title(\n", + " r\"$h_+$ NRTidalv3 ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(chi1,chi2,lambda1, lambda2)\n", + ")\n", + "plt.xlabel(\"Frequency\")\n", + "plt.ylabel(\"Strain\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5940b64e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XXsuqVaua/Dk5GgBOrIFw6NAhnn76acLDw1m6dClDhw7l888/P2WcnTt3EhER0eivMCkpKTg7OxMXF9fs786bN69V9wj+ud69e1NfX09ZWVmjbTt27CAsLKzRyIaUlBSGDRvW6K8TKSkpDUZ1hIWFsWTJEttfu1qrrfHr6+uJjY3lhx9+oLi4mDvvvJMZM2ZQXl7e4PeOHz+Op6dns/+Rqq2tJTc3t0U/zV1np3qd28usWbPYtGkTaWlpjbatXLmSyspKfvOb33RYPi1xqpyhZXmf7nye1JJzMmzYMACefPJJLr74Yq699lrbtrZe3y2N09LrGE597Pa4jqH51y0sLIycnJxG/U+2tWaE1JnsqyXn/VTn/OTn4ezZsxu0X3LJJXh6ejb6PGzr+W9pPHudf+g872WtOb/2vK5O5dlnn2XkyJG2L78nXXLJJVRWVrJt2za7xPmllh5fZ34dWvqeK3IqKgSIdLA333yT4OBg3n333UY/V199NR988IFdh5sB7N+/v8HzAwcOYLVaW71Y2an+4tHUquM/X5EbICgoCA8Pj0b5wIm/OP+8n4+PDxaLhSlTpjT588vFvMLCwrj11ltZuXIl6enp9O7dm4cffviUx7Nz584mp0bs2LGDgQMHNjkk0B5O/jX25yNCfh47Pj6+UXtKSkqj9rq6Ovbt29fgGGbOnMkll1yCv7//GeXW1vheXl488MADREdHYzabueqqq3B1dW1wfuHEsQ8ZMqTZPH788UfCwsJa9NPcyuinep3by8l/uyUlJY22vfnmm3h7ezcabeFop8oZWpb36c7nSS05JydH4vj7+ze6o0hbr++WxmnpdQynPnZ7XMfQ/OuWkJBAWlqabdXxkzZs2GDb3lJnsq+WnPdTnfM333wTFxeXRqOyvLy8uOiiixp9Hrb1/Lc0nr3OP3Se97LWnF97XlenkpeX12Txo66uDjhRkGkPLT2+zvw6tPQ9V+RUVAgQ6UBVVVW8//77XHzxxcyaNavRz4IFCygrK+Ojjz6ya9zk5OQGz59++mkALrzwwlbt5+TKtb/80t+vXz9KSkps88vhRMX8l3cCcHJyYtq0aaxcuZLMzExb+549e/jyyy8b9Lv88st577332LVrV6M8CgoKbI8tFkujLzDBwcGEh4c3OaTv57+3Z8+eZr/0tufaCSeHl27evLlRTqmpqY1yKiwsJCcnp1H7nj17qKurs1uu7RF///79FBUV0b9//wbtW7duZcKECc3+3sl5tS35aW5ebXOvc2tUVlayd+/eButUAE3eXq6uro7XX38dDw+PRqNJCgoK+Prrr7n00ks7ZD5nU3m3Nmdoed6nO58nteScvPTSS8CJv4bZ6y9ubY3T3HUMpz52e1zH0PzrNmvWLCwWS4MpVDU1Nbz22mskJSU1WPvkdM5kXy05783lfvLz8Pzzz6dXr16Nfm/27Nl2/TxsS7wzPf/Qed7LWnN+7XldndTUe9LAgQPZtm1bo9FI//73vzGbze32OdzS4+vMr0NL33NFTkVrBIh0oI8++oiysrJm/7I2btw4goKCePPNNxsNXWyL9PR0LrnkEi644ALWr1/Pv/71L6655pomvwSfyujRowG4//77ueqqq3BxcWHGjBlcddVV3HvvvVx66aXcfvvtVFZW8txzzzFw4EC2bt3aYB8PPvggX3zxBeeccw633nor9fX1PP300wwdOrRBIeGxxx7j22+/JSkpiZtuuom4uDiKiorYunUrX3/9NUVFRQCUlZURGRnJrFmziI+Px9vbm6+//ppNmzbxxBNPNHss+/fvp7q6utEHbFVVFQcOHGDu3Lmtem1ao2/fvgwbNoyvv/66wa33Tub0y/OSkpIC0Kj95OvV2v8smUwmzjvvPNasWdOg3d7xq6qquPbaa1m0aBF+fn629i1btlBUVMSvf/3rZnO0x7za5l7nk5555hmKi4vJzs4G4OOPP+bIkSPAiQU1/fz82LhxI5MmTWLx4sUN5iPfcsstlJaWcu655xIREUFubi5vvvkme/fu5Yknnmi0qNeKFSuor68/7bSA5s5Na3IGmsy7tTm3NO+WnM+TTndODh48yB//+Efgf2t1tNbpXsPWxmnuOobTH7u95oc397olJSVxxRVXsGjRIvLz8+nfvz///Oc/ycjI4JVXXmm0n1O9Nq3dV0vPe3O5n/w8bO6z7qKLLsLHx6dVn4enOr4zjdeW8w+d572sNee3tdfCmb6X3n333Xz++eecc845LFiwgN69e/PJJ5/w+eef89vf/rZBga4l/65b+v7Y0uPrjK8DtO49V+SUHHa/ApEeaMaMGYa7u7tRUVHRbJ958+YZLi4uRmFhoe32ewUFBQ36zJ071/Dy8mr0u+edd54xdOhQ2/OTv5+ammrMmjXL8PHxMXr16mUsWLDAqKqqavC7Lbl9oGGcuMVNRESEYTabG2z/6quvjGHDhhmurq7GoEGDjH/9619N3j7QMAzju+++M0aPHm24uroaffv2NZ5//vkm++bl5Rnz5883oqKiDBcXFyM0NNT41a9+Zbz44ou2PjU1Ncbdd99txMfHGz4+PoaXl5cRHx9/2tuzvfPOOwZg7Nq1q0H7xo0bDcD45JNPGv3ORRddZPj5+Rl+fn6Gi4uL4eHhYXv+6KOPnjLeLz355JOGt7d3g9sgNpfTk08+aQBGcXFxg/Z77rnH8PX1NaxWa6P933LLLcbixYsbtZeVlRmAcdVVVzXaZs/4tbW1xkUXXWRcc801jbbfe++9RnR0dJO/Z29Nvc4nxcTEnPY2Xidv/fXL1/Lf//63MWXKFCMkJMRwdnY2evXqZUyZMsX48MMPm8xj3LhxRnBwsFFfX99srqc6N63Jubm8W5tzS/Nu7fls7pxYrVbjvPPOM3r16mVcf/31hre3d7P7PJPr+0zinOo6NozOcS1XVVUZd911lxEaGmq4ubkZiYmJxhdffNHo91vy2rR0X4bRumNvKvcZM2YYbm5uDW5H+ku/+c1vbJ+HP9fU+T/d8Z1JvK5w/g2j5e8LrTm/renblvfSDRs2GBdeeKERGhpquLi4GAMHDjQefvhho66uztanJddua16H1hxfZ3odTurI6066NxUCRLqx5goJ0nZz5849o3u4n1RcXGwEBAQYL7/8sv2S+pnmvih9+umnhslkMnbs2NEucQ3DMCwWizF79mzj4osvbvSfmOrqaiM0NNRYtmxZu8X/ufZ+ne2pI86NvZ3J+WzunDzzzDMGYLz++uvGv//9bwMwDh482OQ+2nJ9tzTOqa5jw+h617I9r6/WHru9/x02df7t/e+nu53/rqwrvje2l46+7qR70xoBIiIO4Ofnxz333MPSpUttd0mwh/r6eqqrq7FYLA0en/Ttt99y1VVXMXz4cLvF/KVbbrmFnJwc3n33XZydG85Ae+2113BxceF3v/tdu8X/ufZ6ndtDR5wbezuT89nUOcnIyOC+++5jxowZzJkzx/Ya/HJqUVuv75bGgVNfx2d67G3R1mvZntdXa4/dXv8OT3X+7f3vp7ud/66sK743tpeOvu6km3N0JcKRMjMzjfPOO88YMmSIMXz4cOOdd95psH3mzJmGv7+/cfnllzsoQ5G20YiA9tPWEQHt5eQ5//lPR+aZkZFhAIa7u7vh5eVl+1m7dm2H5SBdi9VqNSZPnmz06tXLyM7ONgzDMOrq6gxvb29j4MCBxgsvvGCUl5cbhtG267s1cXQdd04d9f6m8y8iPYHJMFpw0/FuKicnh7y8PBISEsjNzWX06NGkpaXZVkZfs2YNZWVl/POf/+Q///mPg7MVab0lS5bw4IMPUlBQQGBgoKPTERFp5IUXXuB3v/sdr7/+OnPmzLG1L1++nD//+c8UFBRQVlaGi4tLl4gjIiLSFfToQsAvxcfH88knnzS4HciaNWt45plnVAgQERERERGRbqFTrxGwdu1aZsyYQXh4OCaTiZUrVzbqk5ycTGxsLO7u7iQlJbFx48YzirVlyxYsFssZ3RNUREREREREpKvo1IWAiooK4uPjSU5ObnL7ihUrWLhwIYsXL2br1q3Ex8czbdo08vPzbX0SEhIYNmxYo5+T9/gEKCoq4rrrruPFF19s92MSERERERERcaQuMzXAZDLxwQcfMHPmTFtbUlISiYmJPPPMMwBYrVaioqK47bbbuO+++1q035qaGqZOncpNN93UYM7gSS2ZGlBTU0NNTY3tudVqpaioiN69e2MymVp4hCIiIiIiIiJnxjAMysrKCA8Px2w+9d/8G98PpYuora1ly5YtLFq0yNZmNpuZMmUK69evb9E+DMNg3rx5TJ48uckiQEs9+uijPPjgg2f8+yIiIiIiIiL2kJWVRWRk5Cn7dNlCQGFhIRaLhZCQkAbtISEh7N27t0X7WLduHStWrGDEiBG29QfeeOMN231Kp0yZQkpKChUVFURGRvLuu+8yfvz4RvtZtGgRCxcutD0vKSkhOjqarKwsfH19z/AIRURERESkNSoqKggPDwcgOzvbdjew7hZTpCmlpaVERUXh4+Nz2r5dthBgD2effTZWq7XZ7V9//XWL9uPm5oabm1ujdl9fXxUCREREREQ6iJOTk+2xr69vh3wpd0RMkVNpyfT0Tr1Y4KkEBgbi5OREXl5eg/a8vDxCQ0MdklNycjJxcXEkJiY6JL6IiIiIiIjI6XTZQoCrqyujR49m9erVtjar1crq1aubHL7fEebPn09qaiqbNm1ySHwRERERERGR0+nUUwPKy8s5cOCA7Xl6ejrbt28nICCA6OhoFi5cyNy5cxkzZgxjx45l2bJlVFRUcP311zswaxEREREREZHOq1MXAjZv3sykSZNsz08uyDd37lyWL1/O7NmzKSgo4IEHHiA3N5eEhAS++OKLRgsIdpTk5GSSk5OxWCwOiS8iIiIiIt2DxWKhrq7O0WlIJ+Li4tJgTYq2MBmGYdhlT2JTWlqKn58fJSUlWixQRERERKSDVFdXc/nllwPw3nvv4e7u3uViGoZBbm4uxcXFdshOuht/f39CQ0ObXBCwNd9DVQhoByoEiIiIiIjImcjJyaG4uJjg4GA8PT1btAK8dH+GYVBZWUl+fj7+/v6EhYU16tOa76GdempAV6OpASIiIiIicqYsFoutCNC7d29HpyOdjIeHBwD5+fkEBwe3aZpAl71rQGekuwaIiIiIiMiZOrkmgKenp4Mzkc7q5LXR1vUjVAgQEREREZFuoaKiAi8vL7y8vKioqOiyMTUdQJpjr2tDUwNERERERKTbqKys7BExRdpCIwLsKDk5mbi4OBITEx2dioiIiIiIiEiTVAiwI60RICIiIiIiPdG8efOYOXPmKfscOXIEV1dXhg0b1uR2k8nEypUr7Z+cNKJCgIiIiIiIiLS75cuXc+WVV1JaWsqGDRscnU6PpkKAiIiIiIiItCvDMHjttdeYM2cO11xzDa+88oqjU+rRtFigiIiIiIhIJ2UYBlV1lg6P6+HiZNe7F3z77bdUVlYyZcoUIiIimDBhAk899RReXl52iyEtp0KAHSUnJ5OcnIzF0vH/UEVEREREejqz2cx5551ne9wdYlbVWYh74Eu77/d0Uh+ahqer/b4uvvLKK1x11VU4OTkxbNgw+vbty7vvvsu8efPsFkNaToUAO5o/fz7z58+ntLQUPz8/R6cjIiIiItKjeHh4sGbNmm4fs6spLi7m/fff54cffrC1XXvttbzyyisqBDiICgEiIiIiIiKdlIeLE6kPTXNIXHt56623qK6uJikpydZmGAZWq5W0tDQGDhxot1jSMioEiIiIiIiIdFImk8muQ/Qd4ZVXXuEPf/hDo7/+33rrrbz66qs89thjjkmsB+vaV5SIiIiIiMh/VVRUEBsbC0BGRkaHLETniJidVUlJCdu3b2/QVlZWxtatW3nzzTcZPHhwg21XX301Dz30EH/9619xdj7x1TQ9Pb3RPgYMGNCjX9f2oEKAHWmxQBERERERxyosLOwRMTujNWvWMHLkyAZt8+bNIy4urlERAODSSy9lwYIFfPbZZ1xyySUALFy4sFG/77//nrPPPrt9ku6hTIZhGI5Oors5uVhgSUkJvr6+jk5HRERERKRHqKiowNvbG4Dy8vIOGxFgr5jV1dWkp6fTp08f3N3d7ZWidCOnukZa8z20Y+6pISIiIiIiIiKdggoBIiIiIiIiIj2ICgEiIiIiIiIiPYgKASIiIiIiIiI9iO4aICIiIiIi3YLZbGbMmDG2x901pkhbqRAgIiIiIiLdgoeHB5s2ber2MUXaSiUrO0pOTiYuLo7ExERHpyIiIiIiIiLSJBUC7Gj+/PmkpqaqIigiIiIiIiKdlgoBIiIiIiLSLVRWVhIbG0tsbCyVlZXdNqZIW6kQICIiIiIi3YJhGBw+fJjDhw9jGEa3jdlVmEwmVq5cadd9Ll++HH9/f7vusyfSYoEiIiIiIiJidzk5OfTq1cvRaUgTVAgQERERERERu6qtrSU0NNTRaUgzNDVARERERERE2mTixIksWLCAO++8k8DAQKZNm9ZgakBGRgYmk4m3336bCRMm4O7uzrBhw/juu+9s+1izZg0mk4lPP/2UESNG4O7uzrhx49i1a9cpY3/44YeMGjUKd3d3+vbty4MPPkh9fX17Hm6Xp0KAiIiIiIhIZ1db0fxPXXUr+ladvu8Z+uc//4mrqyvr1q3j+eefb7LP3XffzR/+8Ae2bdvG+PHjmTFjBseOHWvU54knnmDTpk0EBQUxY8YM6urqmtzf999/z3XXXccdd9xBamoqL7zwAsuXL+fhhx8+4+PoCTQ1QEREREREpLN7JLz5bQPOh9+8+7/nS/tDXTN3MIg5G67/9H/Plw2HyoZfxFlSckYpDhgwgL///e+n7LNgwQIuv/xyAJ577jm++OILXnnlFe655x5bn8WLFzN16lTgRHEhMjKSDz74gCuvvLLR/h588EHuu+8+5s6dC0Dfvn35y1/+wj333MPixYvP6Dh6AhUCRERERESkWzCZTMTFxdked9eYndXo0aNP22f8+PG2x87OzowZM4Y9e/Y02ycgIIBBgwY16nNSSkoK69atazACwGKxUF1dTWVlJZ6enq09jB5BhQA7Sk5OJjk5GYvF4uhURERERER6HE9PT3bv3t09Y/4xu/ltJqeGz+8+cIq+v5gdfufOM8/pF7y8vOy2r5YqLy/nwQcf5LLLLmu0zd3dvcPz6SpUCLCj+fPnM3/+fEpLS/Hz83N0OiIiIiIi0l24tuJLdnv1tYOffvqJc889F4D6+nq2bNnCggULGvWJjo4G4Pjx46SlpTFkyJAm9zdq1Cj27dtH//792zfxbkaFABEREREREekQycnJDBgwgCFDhvDUU09x/PhxbrjhhgZ9HnroIXr37k1ISAj3338/gYGBzJw5s8n9PfDAA1x88cVER0cza9YszGYzKSkp7Nq1i7/+9a8dcERdk+4aICIiIiIi3UJlZSVDhw5l6NChVFY2s1heN4jZlT322GM89thjxMfH88MPP/DRRx8RGBjYqM8dd9zB6NGjyc3N5eOPP8bV1bXJ/U2bNo1PPvmEr776isTERMaNG8dTTz1FTExMRxxOl6URASIiIiIi0i0YhkFqaqrtcXeN2RmtWbOmUVtTr8eQIUPYsGHDKfd19tlns2vXria3zZs3j3nz5jVomzZtGtOmTWtxrqIRASIiIiIiIiI9igoBIiIiIiIiIj2IpgaIiIiIiIhIu4qNjT3t1ImJEyf26OkVHUkjAkRERERERER6EBUCRERERERERHoQTQ0QEREREZFuwWQy2W4bZzKZum1Mkbbq0SMCsrKymDhxInFxcYwYMYJ33323RdtERERERKTz8fT0JCMjg4yMDDw9PbttTJG26tEjApydnVm2bBkJCQnk5uYyevRopk+fjpeX1ym3iYiIiIiIiHRVPboQEBYWRlhYGAChoaEEBgZSVFSEl5fXKbeJiIiIiIiIdFWdemrA2rVrmTFjBuHh4ZhMJlauXNmoT3JyMrGxsbi7u5OUlMTGjRvPKNaWLVuwWCxERUW1apuIiIiIiHQOVVVVJCYmkpiYSFVVVbeN2RUtX74cf3//Do25ZMkSEhISOjRmV9GpCwEVFRXEx8eTnJzc5PYVK1awcOFCFi9ezNatW4mPj2fatGnk5+fb+iQkJDBs2LBGP9nZ2bY+RUVFXHfddbz44ouNYpxqm4iIiIiIdB5Wq5XNmzezefNmrFZrt43ZGc2bNw+TyYTJZMLV1ZX+/fvz0EMPUV9f7+jUWqW5P0B3N516asCFF17IhRde2Oz2J598kptuuonrr78egOeff55PP/2UV199lfvuuw+A7du3nzJGTU0NM2fO5L777mPChAkt3vbLfjU1NbbnpaWlpzs0ERERERGRbuWCCy7gtddeo6amhs8++4z58+fj4uLCokWLHJ1al1RbW4urq2u77LtTjwg4ldraWrZs2cKUKVNsbWazmSlTprB+/foW7cMwDObNm8fkyZOZM2dOi7f90qOPPoqfn5/tR1MIRERERESkp3FzcyM0NJSYmBh+//vfM2XKFD766KMm+x48eJBf//rXhISE4O3tTWJiIl9//XWDPrGxsTzyyCPccMMN+Pj4EB0d3Wik9pEjR7j66qsJCAjAy8uLMWPGsGHDhmZj9u3blwULFmAYRqPtsbGxAFx66aWYTCbb85bm+pe//IWrr74aLy8vIiIiGo1sLy4u5re//S1BQUH4+voyefJkUlJSbNtPTmV4+eWX6dOnD+7u7k0ehz102UJAYWEhFouFkJCQBu0hISHk5ua2aB/r1q1jxYoVrFy5koSEBBISEti5c+dpt/3SokWLKCkpsf1kZWW17eBERERERER+pqKiotmf6urqFvf95ToGTfWxFw8PD2pra5vcVl5ezvTp01m9ejXbtm3jggsuYMaMGWRmZjbo98QTTzBmzBi2bdvGrbfeyu9//3v27dtn28d5553H0aNH+eijj0hJSeGee+5pcorGjh07OPvss7nmmmt45plnMJlMjfps2rQJgNdee42cnBzb85bmunTpUuLj49m2bRv33Xcfd9xxB6tWrbJtv+KKK8jPz+fzzz9ny5YtjBo1il/96lcUFRXZ+hw4cID33nuP999//7Sj29uiU08NaG9nn312s/N4TrXtl9zc3HBzc7NnaiIiIiIiIjbe3t7Nbps+fTqffvqp7XlwcDCVlZVN9j3vvPNYs2aN7XlsbCyFhYUN+jT11/LWMAyD1atX8+WXX3Lbbbc12Sc+Pp74+Hjb87/85S988MEHfPTRRyxYsMDWPn36dG699VYA7r33Xp566im+/fZbBg0axFtvvUVBQQGbNm0iICAAgP79+zeK9eOPP3LxxRdz//3384c//KHZvIOCggDw9/cnNDS01bmeddZZtinqAwcOZN26dTz11FNMnTqVH374gY0bN5Kfn2/77vj444+zcuVK/vOf/3DzzTcDJ0a+v/7667Zc2kuXLQQEBgbi5OREXl5eg/a8vLwGJ60jJScnk5ycjMVicUh8ERERERERR/nkk0/w9vamrq4Oq9XKNddcw5IlS5rsW15ezpIlS/j000/Jycmhvr6eqqqqRn9lHzFihO2xyWQiNDTUtjj89u3bGTlypK0I0JTMzEymTp3Kww8/zJ133nlGx9XSXMePH9/o+bJlywBISUmhvLyc3r17N+hTVVXFwYMHbc9jYmLavQgAXbgQ4OrqyujRo1m9ejUzZ84ETqzYuXr16gZVmY40f/585s+fT2lpKX5+fg7JQURERESkJwsMDOyWMcvLy5vd5uTk1OD5z++i9ktmc8PZ4RkZGW3K6+cmTZrEc889h6urK+Hh4Tg7N/9186677mLVqlU8/vjj9O/fHw8PD2bNmtVoKoGLi0uD5yaTyTZy28PD47Q5BQUFER4ezr///W9uuOEGfH19W31cLc31VMrLywkLC2swGuOkn99W0cvLq9X5nYlOXQgoLy/nwIEDtufp6els376dgIAAoqOjWbhwIXPnzmXMmDGMHTuWZcuWUVFRYbuLgIiIiIiI9BxeXl4UFBR0y5it+YLYXn1bsq+mhuY3Zd26dcybN49LL70UOPHdr7VFiREjRvDyyy9TVFTU7KgADw8PPvnkE6ZPn860adP46quv8PHxaXafLi4ujUZ4tzTXn376qdHzIUOGADBq1Chyc3Nxdna2LULoSJ16scDNmzczcuRIRo4cCcDChQsZOXIkDzzwAACzZ8/m8ccf54EHHiAhIYHt27fzxRdfNFpAsKMkJycTFxdHYmKiQ+KLiIiIiIh0BQMGDLAtiJeSksI111zT4jXaTrr66qsJDQ1l5syZrFu3jkOHDvHee+81uoucl5cXn376Kc7Ozlx44YWnHF0RGxvL6tWryc3N5fjx463Kdd26dfz9738nLS2N5ORk3n33Xe644w4ApkyZwvjx45k5cyZfffUVGRkZ/Pjjj9x///1s3ry5VcdtD526EDBx4kQMw2j0s3z5clufBQsWcPjwYWpqatiwYQNJSUkOy3f+/PmkpqbaVpcUERERERGRxp588kl69erFhAkTmDFjBtOmTWPUqFGt2oerqytfffUVwcHBTJ8+neHDh/PYY481mioBJxZb/PzzzzEMg4suuqjZuyM88cQTrFq1iqioKNsfpFua6x/+8AfbH7P/+te/8uSTTzJt2jTgxJSGzz77jHPPPZfrr7+egQMHctVVV3H48GGH/CHbZLR1SUhp5OQaASUlJWc0B0VERERERFqvqqqKCy+8EIDPP/+8RXPIO1PM6upq0tPT2/0e8mJ/sbGx3HnnnWe8IGFLneoaac330E69RkBXo7sGiIiIiIg4jtVq5bvvvrM97q4xRdqqU08N6Go0NUBEREREREQ6O40IEBEREREREWkDe96GsSNoRICIiIiIiIhID6JCgB3p9oEiIiIiItJWWs9dmmOva0OFADvSGgEiIiIiInKmXFxcAKisrHRwJtJZnbw2Tl4rZ0prBIiIiIiISLfh6enZZWM6OTnh7+9Pfn6+bb8mk8ku+5auzTAMKisryc/Px9/fHycnpzbtT4UAERERERHpFry8vKioqOjSMUNDQwFsxQCRn/P397ddI22hQoCIiIiIiEgnYTKZCAsLIzg4mLq6OkenI52Ii4tLm0cCnKRCgB0lJyeTnJyMxWJxdCoiIiIiItKFOTk52e1Ln8gvmQwtSWl3paWl+Pn5UVJSgq+vr6PTERERERHpEaqrq7n88ssBeO+993B3d++WMUWa0prvoRoRICIiIiIi3YLFYuGzzz6zPe6uMUXaSrcPFBEREREREelBVAgQERERERER6UFUCBARERERERHpQVQIsKPk5GTi4uJITEx0dCoiIiIiIiIiTVIhwI7mz59PamoqmzZtcnQqIiIiIiIiIk1SIUBERERERESkB9HtA0VEREREpFvw8vLCMIxuH1OkrTQiQERERERERKQHUSFAREREREREpAdRIUBERERERLqF6upqrrjiCq644gqqq6u7bUyRtjIZmtBiN8nJySQnJ2OxWEhLS6OkpARfX19HpyUiIiIi0iNUVFTg7e0NQHl5OV5eXt0ypkhTSktL8fPza9H3UI0IsCPdPlBEREREREQ6OxUCRERERERERHoQFQJEREREREREehAVAkRERERERER6EBUCRERERERERHoQFQJEREREREREehBnRycgIiIiIiJiD56enpSXl9sed9eYIm2lQoCIiIiIiHQLJpMJLy+vbh9TpK00NUBERERERESkB1EhwI6Sk5OJi4sjMTHR0amIiIiIiPQ4NTU1zJs3j3nz5lFTU9NtY4q0lckwDMPRSXQ3paWl+Pn5UVJSgq+vr6PTERERERHpESoqKvD29gagvLy8Q4bsOyKmSFNa8z1UIwJEREREREREehAVAkRERERERER6EBUCRERERERERHoQFQJEREREREREehAVAkRERERERER6EBUCRERERERERHoQZ0cnICIiIiIiYg+enp7k5+fbHnfXmCJtpUKAiIiIiIh0CyaTiaCgoG4fU6StNDVAREREREREpAdRIUBERERERLqFmpoa5s+fz/z586mpqem2MUXaymQYhuHoJBwlKyuLOXPmkJ+fj7OzM3/+85+54oorACguLmbKlCnU19dTX1/PHXfcwU033dSi/ZaWluLn50dJSQm+vr7teQgiIiIiIvJfFRUVeHt7A1BeXo6Xl1e3jCnSlNZ8D+3RawQ4OzuzbNkyEhISyM3NZfTo0UyfPh0vLy98fHxYu3Ytnp6eVFRUMGzYMC677DJ69+7t6LRFREREREREzliPLgSEhYURFhYGQGhoKIGBgRQVFeHl5YWTk5Nt1c+amhoMw6AHD54QERERERGRbqJTrxGwdu1aZsyYQXh4OCaTiZUrVzbqk5ycTGxsLO7u7iQlJbFx48YzirVlyxYsFgtRUVG2tuLiYuLj44mMjOTuu+8mMDDwTA9FREREREREpFPo1IWAiooK4uPjSU5ObnL7ihUrWLhwIYsXL2br1q3Ex8czbdo02308ARISEhg2bFijn+zsbFufoqIirrvuOl588cUG+/f39yclJYX09HTeeust8vLy2udARURERERERDpIl1ks0GQy8cEHHzBz5kxbW1JSEomJiTzzzDMAWK1WoqKiuO2227jvvvtatN+amhqmTp3KTTfdxJw5c5rtd+uttzJ58mRmzZrV5D5+vkJoaWkpUVFRWixQRERERKQDabFA6clas1hgpx4RcCq1tbVs2bKFKVOm2NrMZjNTpkxh/fr1LdqHYRjMmzePyZMnNyoC5OXlUVZWBkBJSQlr165l0KBBTe7n0Ucfxc/Pz/bz8+kFIiIiIiIiIp1Jly0EFBYWYrFYCAkJadAeEhJCbm5ui/axbt06VqxYwcqVK0lISCAhIYGdO3cCcPjwYc455xzi4+M555xzuO222xg+fHiT+1m0aBElJSW2n6ysrLYdnIiIiIiItJqHhwfp6emkp6fj4eHRbWOKtFWPvmvA2WefjdVqbXLb2LFj2b59e4v24+bmhpubG8nJySQnJ2OxWOyYpYiIiIiItITZbCY2NrbbxxRpqy47IiAwMBAnJ6dGC/jl5eURGhrqkJzmz59PamoqmzZtckh8ERERERERkdPpsoUAV1dXRo8ezerVq21tVquV1atXM378eAdmJiIiIiIijlBbW8vdd9/N3XffTW1tbbeNKdJWnfquAeXl5Rw4cACAkSNH8uSTTzJp0iQCAgKIjo5mxYoVzJ07lxdeeIGxY8eybNky3nnnHfbu3dto7YCO8POpAWlpabprgIiIiIhIB9JdA6Qna81dAzp1IWDNmjVMmjSpUfvcuXNZvnw5AM888wxLly4lNzeXhIQE/vGPf5CUlNTBmTbUmhMgIiIiIiL2oUKA9GTdphDQVakQICIiIiLS8VQIkJ6sNd9Du+waASIiIiIiIiLSeioE2FFycjJxcXEkJiY6OhURERERERGRJqkQYEdd+faBpdV1HC4oxWqxODoVERERERERaUfOjk5AOodv9uTz4buv8Q+XZ8hy7UeZ/2DMYSPo1W80UYNG4ebu6egURURERERExA5UCBAACstrGO6UiY+piri6XVCwCwr+AzugznDikFMUn0TfhWe/s4gL9yUu1Ad/LzdHpy0iIiIiYuPh4cGuXbtsj7trTJG20l0D7Cg5OZnk5GQsFgtpaWld7q4BdbU1ZB1I4dj+LViyU/Au3kNkzQH8KQfg/Jq/kWZEAXC90+fc7PIFuZ4DqO49FPeoBEIGJhIWPQCTWTNOREREREREOpJuH+hg3en2gYbVSt7RQ+Tu28T31gR25VWQmlPKHWVPMctpbaP+pXhyxLUfqwY+SESfQcSF+dI/2BtXZxUHRERERERE2osKAQ7WnQoBzSk5XsjRvZsoy9iCOW8XAWX7iKo/jKvJgtUwMbTmFapwB+DPLv9iouteinwGYQ0Zhk/MKCLjxuLrH+jgoxARERGR7qS2tpZHHnkEgD/+8Y+4urp2y5giTVEhwMF6QiGgKbU11WSlbSc/YzerGM/u7BJSc0p51fonEs1pjfpnm0LI8xzAuvi/MygykKHhvoT5uWMymRyQvYiIiIh0dRUVFXh7ewNQXl6Ol5dXt4wp0pTWfA/VYoF29PM1AnoiVzd3+g0fR7/h4xj/3zbDMMjJ7MO2fRupztqO+7HdhFbuJ4wCwo08TOV1PP5NBpABwHPuyUS5VVDuPwSn8BEE9h9D5IB4XFy1MKGIiIiIiIg9aERAO+ipIwJao6Qon6w9G8nJzeVzyxhSs0s5kF/OepffEWQqadC3xnAh0zmGXP9RHBp9P3HhvgwJ88XbTXUsEREREfkfjQiQnkwjAqTT8wsIxu+sixkGTP1vW01dPVmp/ybj4GasOTvwKd5LdO1BvE1VDLAc4FiBC4s/2m3bx3uej2Hy9Ke2dxwe0SMJG5xEUFiM7logIiIiIiJyCioESKfh5uJM//izIP4sW5vVYuHo4TTy0jaQXWLlV1XBpOaUUlZSxGjrDigHytfCYeB7OI4vR936kR0yiapRvyUuzJc+gV44O6k4ICIiIiIiAioESCdndnIiou8QIvoOYRRw2X/bi0rL2ZXyOmWHt+Gcv4vA8jSiLFn0MpXSq2Ybuw75cV/adgC8na38x/2vlPoOwAgdgV+fUUQNHoOXj7+DjkpERERERMRxVAiQLinA15uAc34N5/za1lZdVUH63i0cP7SFiqpARpf1Yk9OKdF1GQyu3wtFe6HoY0gF6ycmssxh5HsNJC92Bp7DLyEu3JdgH3cHHpWIiIiIiEj7UyHAjnr6XQMczd3DiwEjz4WR5zIWuBGwWg2ycnLYkuJJ7dHteBalElZ1gGBTEVFGNlHl2fxtWyjPbQ4DIN7rOH91eZWKXnG4RMYTNCCRiL7DcHLWPxURERGRzs7d3Z2NGzfaHnfXmCJtpbsGtAPdNaDzK8zNImffJioOb+M7y3BWHQ/hUEE500wbeM71/xr0rTJcyXTpQ4nfYIoGXknIkLMYHOqLh6uTg7IXERERERFpqDXfQ+1eCCguLuY///kPBw8e5O677yYgIICtW7cSEhJCRESEPUN1WioEdE1VtRYOHkilYveXkLsT/5K9RNWl42mqsfX5fe0dfG5NwmyCS3plMsfpK+qChuEVM5LwwWPpHRLpwCMQEREREZGeymG3D9yxYwdTpkzBz8+PjIwMbrrpJgICAnj//ffJzMzk9ddft2c4EbvycHViWNxwiBtua7PU13P44C4KDmym7kgKbqaxBOa7UlheS1jJdka7fANl38Ah4FsooBc57v2p7B1H5bA59BkQR0yAJ2azyXEHJiIiItJD1NbW8n//d2J05x133IGrq2u3jCnSVnYdETBlyhRGjRrF3//+d3x8fEhJSaFv3778+OOPXHPNNWRkZNgrVKemEQHdX35ZNYd3rac+7WtcCncTXJ5GpDUbs+l//5ym1zxCqhGLl6sT83rt5FyXVExhI+jVdzRRg0bh7untwCMQERER6X4qKirw9j7xf6zy8nK8vLy6ZUyRpjhsRMCmTZt44YUXGrVHRESQm5trz1AiDhXs407w+EkwfpKtraKsmKy9mylJ3wq5O/EwxeGWV01FrYWoYz+Q5LwGCt+HnVBvmMlwiqTQexD1wcNg1HUMiomkl5cqyCIiIiIi0r7sWghwc3OjtLS0UXtaWhpBQUH2DCXS6Xj5+DM4cQokTgHgPaDeYiW9sIKC7eWsT4/A+/geImv208tURqw1k9jSTChdxbBdQylnD2F+7tzi/QODPIpxj4wnZOBYwmIHYTJrYUIREREREbEPuxYCLrnkEh566CHeeecdAEwmE5mZmdx7771cfvnl9gzVKen2gfJLzk5mBoT4MGDabGA2AIbVSn7OYXL2baAyczt1x4/S2yuQ8mOV5JRUM7jqc8aZ98AR4CcoNzzIcu1Lqf8QzGEj8Eicw4AwP9ycVRwQEREREZHWs+saASUlJcyaNYvNmzdTVlZGeHg4ubm5jB8/ns8++6zHzJfRGgFyJsqq69ibW0btptdxyd5Er9K9RNcfxs1UZ+tTYPiRWPMczmYT/YO9+b3b54T4euAdO5LowUn49g524BGIiIiIOJbWCJCezGFrBPj5+bFq1Sp++OEHduzYQXl5OaNGjWLKlCn2DCPSLfm4u5AYGwCxd9ra6mprOHQghWP7t1CfnUJ+hRU/swslVSeKBmPd3iYsrwj2A6sglyByPAdQ3XsorjGJBI+aQWQvD0wm3bVAREREREROsOuIADlBIwKkPRmGQU5JNbuPHMd74//hfmwXIZX7CTfyGvTbZB3IFbVL8HV3Ji7cl1t4H+/gaAL6jiFq0Ehc3dwddAQiIiIi7UMjAqQn69ARAf/4xz9a3Pf2229vaziRHs9kMhHu70G4vwcM+5utveR4IUf3bqI0fSvm/F3sqgnGxWKitLqelEPZnOf2EuZsA7ZDreHEQecYinwGYQ0Zhmf/s4kZfha+7i6OOzARERGRNnJ3d+fbb7+1Pe6uMUXaqs0jAvr06dPgeUFBAZWVlfj7+wNQXFyMp6cnwcHBHDp0qC2hugyNCJDOorbeyoH8cvZnHCZk+9P4FO8hqvYgvlQ06Pdu/bncXf87ogI8GBbqydz693GPSiBsUCLBkf0wmc0OOgIREREREWmJDh0RkJ6ebnv81ltv8eyzz/LKK68waNAgAPbt28dNN93ELbfc0tZQItJKrs5m4sJ9iQsfDhNeBE7ctSA7cz+5aZuoyUrB/dgu9tXFQxlkFVXheXwf49xegExgHRTjzRG3/pT7D8E5YgQBg88jqt8QXJxUHBARERER6YrsukZAv379+M9//sPIkSMbtG/ZsoVZs2Y1KBp0ZxoRIF1RcWUtqTmlZO/fTsyeF+ldto8oSxYupoa3w1xadyUvmS5jUIgPYwPrON+0Hr8+o4gcMhZv3wAHZS8iIiICdXV1vPjiiT9+3Hzzzbi4tP+0R0fEFGlKa76H2rUQ4OnpyXfffUdiYmKD9o0bNzJx4kQqKyvtFapTUyFAuovqqkqOpG3j+MHNWHN24FO8l6dqZ7KqJg6AaeZNvOD6lK3/UVMoeV4Dqe0dh0f0SEKHnktwSJjuWiAiIiIdQosFSk/msELAjBkzOHr0KC+//DKjRo0CTowGuPnmm4mIiOCjjz6yV6hOTYUA6c6sVoOs45WkZpdStnc1Aw+9QVjVfkI41qjvgtrbWO9xHnHhvpztf5wxrhkE9k8kov9wnF1cHZC9iIiIdGcqBEhP1qFrBPzcq6++yty5cxkzZoxtSEx9fT3Tpk3j5ZdftmcoEXEQs9lETG8vYnp7wfBrgWsBKMrP5ui+TZQf3oZz/i4Cy9PYSyzHKmr5fn8hQ5w+ZrTLv2ELVBsuHHLpQ7HvIIzQ4fjFjiZq6Di8vLwde3AiIiIiIj2AXUcEnJSWlsbevXsBGDx4MAMHDrR3iE4pOTmZ5ORkLBYLaWlpGhEgPV51nYW0vDJ2Z5fiuutt4nI+ILr2EF6m6kZ9Z9T+lYqA4cSF+3Kuz1EGe1cTPiSJwNBoB2QuIiIiXZFGBEhP5rCpAXKCpgaINM9qsXA0fQ/5aZuoPbIdz+OphFQd4rzqx6nhxHSBR5xf5hrnbwAoxJ9s9/5UBsThEjGCoAGJRPQbjpOTkyMPQ0RERDohFQKkJ3PY1IAbbrjhlNtfffVVe4YTkS7I7OREVP9hRPUf1qD9h7Ia9uSUkppTSq8dURw+Hkmk5SiBpmICqzdD9mbIBjbBWMurRISFEhfmy7ke6fQJ8iFq8Bg8vHwcc1AiIiIiIl2IXQsBx48fb/C8rq6OXbt2UVxczOTJk+0ZSkS6mSAfN4J8gjh3YBCc9w8AKitKydqzmeL0rZC7E7+SvZjrysmvcyc/s5htmcVMd3mMgU67sXxk4rBTBIVeA6kLGoZX7EgiBo0lICTSwUcmIiIiItK52LUQ8MEHHzRqs1qt/P73v6dfv372DCUiPYCnly+DxkyGMf8rJFosVr4+VklqTimp2aWYdwZTWHli5ECM9QgxZUeg7Bs4BEWrvUlyXU5cuB9x4b6c5XqQqKhoIvrEYdbUAhERkW7Hzc2NTz75xPa4u8YUaasOWSNg3759TJw4kZycnPYO1SlojQCRjleYk8nRvRupzNyGS+FugsvTyLAEcF3tIlufH9xuJ9JUSKXhRqZrP0r8BmMKG0GvvqOJGjwadw/N6RMRERGRrslhawQ05+DBg9TX13dEKBHpoQLDogkMiwZm2dp6V9XwXn4Fqdml7DtaSOXeAKrrSvA01TC4LhUKU6HwfdgJWz8YwKJeTxIX7ktcmC9JLvuJHhCPf2Co4w5KRERERKQd2LUQsHDhwgbPDcMgJyeHTz/9lLlz59ozlIjIaXl5uDE6xo3RMQFALLCJ+rpaDh/YScGBzdQdTcH7+B4ia/aTao1mX14Z+/LK+GxbOqlu1+P0pUEevcnxGEh17zhcoxIIHTiWsJiBmMxmBx+diIiI/FJdXR1vvvkmAL/5zW9wcXHpljFF2squUwMmTZrU4LnZbCYoKIjJkydzww034OzcIQMQHE5TA0S6FsNqJe94GakF1ew+Wkp+5h5uybybSCO3yf7vMZl3wu5haLgfcWFejHLPJWpgAi6umhcoIiLiSLp9oPRkDpsa8O2339pzdyIiHcJkNhPa24/Q3n5MHhwCDAAuoaykiCN7N1JyaCtOeTvpVbaP6PoM9tSHsSG9iA3pRfQzHWW1293UGs4ccI6hyHcwRugI/GJHERU3Fi8ffwcfnYiIiIhIQ3YtBEyePJn3338ff3//Bu2lpaXMnDmTb775xp7hRETalY9fAEOSLoCkC2xttTXVXJ5bzKCCOlJzSjGnZ1B2zAMfUxX9LQfh+EE4/insAetnJpLdbiA19lqGhvsyLMiFuN5mAkOjHHhUIiIiItLT2bUQsGbNGmpraxu1V1dX8/3339szlF1kZWUxZ84c8vPzcXZ25s9//jNXXHFFgz6VlZUMGTKEK664gscff9xBmYpIZ+Hq5s6QmFCGxJxsGYrV8juOHk4jL20DNUdS8Dy2m7Cq/QSbithe7seqHTl8uiOHX5m38IrrExTQi2yPAVQGxOEWmUDwgETC+wzRLQ1FREREpEPYpRCwY8cO2+PU1FRyc/83r9ZisfDFF18QERFhj1B25ezszLJly0hISCA3N5fRo0czffr0BvN6Hn74YcaNG+fALEWkszM7ORHRdwgRfYc0aD+Wd4S5x6yMyj8xemBgxrdYq00EmY4TVLURjm6Eo8AGKDc8WBZwP/V9JhMX7svQQGcGhPXC1c3dMQclIiIiIt2WXQoBCQkJmEwmTCYTkydPbrTdw8ODp59+2h6h7CosLIywsDAAQkNDCQwMpKioyFYI2L9/P3v37mXGjBns2rXLkamKSBfUOySSs0Pg7LiTLSOpLP8TmXs2U3JoC6a8HfQq3Ud0XQbepiq+y3Vlf04GADc6fca9zv/moHMMRT6DsIQMxzd2FFFxSfj4BTjqkERERESkG7BLISA9PR3DMOjbty8bN24kKCjIts3V1ZXg4GCczmDI69q1a1m6dClbtmwhJyeHDz74gJkzZzbok5yczNKlS8nNzSU+Pp6nn36asWPHtjrWli1bsFgsREX9b+7uXXfdxdKlS/nxxx9bvT8RkaZ4evszOHEKJE6xtdXV1pB+YAe3VgaxO7eS3dmlDMnOwRUL/SyH6Fd8CIo/h33Al3DUFMILUUsJjo07ceeCYDeC/X10S0MRERERaRG7FAJiYk5MlrVarfbYnU1FRQXx8fHccMMNXHbZZY22r1ixgoULF/L888+TlJTEsmXLmDZtGvv27SM4OBg4MVqhvr6+0e9+9dVXhIeHA1BUVMR1113HSy+9ZNv+4YcfMnDgQAYOHKhCgIi0KxdXN/rEJdIHuPS/bYb1P+Rk7Sd33yaqs7bjfmw3YZVphFJIqDWfd9IsVKelAfCQ82tc7LyBo+4DKO81FNfIEQQNSCSy7zDMPeS2rSIiIgBubm688847tsfdNaZIW5kMwzDasoOPPvqICy+8EBcXFz766KNT9r3kkkvOOI7JZGo0IiApKYnExESeeeYZ4EQhIioqittuu4377ruvRfutqalh6tSp3HTTTcyZM8fWvmjRIv71r3/h5OREeXk5dXV1/OEPf+CBBx5och81NTW256WlpURFRbXo/o0iIq1xvDCXrP0p/FTXn9TsUnZnl/Jo8V2MMac16ltpuJHp2pcVQ5IZFBnE0HA/BgR74e6q4oCIiIhId1NaWoqfn1+Lvoe2uRBgNpvJzc0lODgY8ymGpZpMJiwWyxnH+WUhoLa2Fk9PT/7zn/80KA7MnTuX4uJiPvzww9Pu0zAMrrnmGgYNGsSSJUua7bd8+XJ27drV7F0DlixZwoMPPtioXYUAEekIVRXlZO3bzPGDWyB3B34le4mpO4SHqZZsI4AJNc/Y+r7qupQ+zkUc8xlIffBwfGJGETUkCd/ewQ48AhERERFpq9YUAtr8Z6GfTwew99SAUyksLMRisRASEtKgPSQkhL1797ZoH+vWrWPFihWMGDGClStXAvDGG28wfPjwVuWyaNEiFi5caHt+ckSAiEhH8PDyZuCoiTBqoq2tvq6OjIM7OXIkk5tr+54YPXC0mBHWgwRaS+lTchhKVsF+4GvIMQWR7j2STfGPMDTcl7hwX8L83DGZTI46LBERkVarr6/ngw8+AODSSy/FuQOmyDkipkhb9eir9Oyzz25R8WLevHmn3O7m5qb5QCLSqTi7uBA7eBSxg0dx9n/bDKuVvOzv2L53I1WZ23Ar3E1oVRrhRh5hRgEZxUd46uv/TTH4xP3PmNx8KOsVh0vEiXUHIvrH4+Ts4piDEhEROY2amhquvPJKAMrLyzvkS7kjYoq0VZuv0n/84x8t7nv77be3NZxNYGAgTk5O5OXlNWjPy8sjNDTUbnFaIzk5meTk5DZNgRARaS8ms5nQyL6ERvYFrrK1lxQVkrV3A8cKK7msKoLUnFJy8vMZxkGoAXK3Q+5bsAWqDRcOuvThUNBkjiXcytBwPwaH+uDu0vo7w4iIiIiIY7R5jYA+ffq0LJDJxKFDh844TnOLBY4dO5ann34aODE1ITo6mgULFrR4scD20Jq5GSIinVF1TQ1ZezZSdHArRk4KfiV7iK49hJepGoC36ifxx/qbAHA31fG5xwMc9+pHXfAwvGJHEzl4LP5B4Y48BBER6YEqKirw9vYGTvx13svLq1vGFGlKh64RkJ6e3tZdNKu8vJwDBw40iLV9+3YCAgKIjo5m4cKFzJ07lzFjxjB27FiWLVtGRUUF119/fbvlJCLSE7i7uTEg4RxIOMfWZrFYyDy0m/z9m6kv9+GcskBSs0sJrUynj/UwfcoOQ9k3cBBYDfkEkOMxgKzombiMuIyh4b5E+Hto3QERERERB2u3CSwnBxq05T98mzdvZtKkSbbnJxfkmzt3LsuXL2f27NkUFBTwwAMPkJubS0JCAl988UWjBQQ7iqYGiEh35uTkRPSAEUQPGMEY4DpOvNcXHBvJ9p0hVB3ehmvhLoIr9hNlZBNMEcFVG/hsV19eSIkBYKj7MZa6vkSZ/xCcwkcQOCCRyAEJOLtqnRURERGRjtLmqQG/9Morr/DUU0+xf/9+AAYMGMCdd97Jb3/7W3uG6dQ0NUBEerrSkiKy9myiLH0rP9QPZnVRIPvzypjGepJdG64tU2s4k+kcw3HfwRQOvIrguHMYEuaDp6sWWxIRkdbR1ADpyTp0asDPPfDAAzz55JPcdtttjB8/HoD169fz//7f/yMzM5OHHnrInuFERKST8vULYOi4aTBuGuOAu4CaegvpB/uycXcw1pwd+BanElV7EB9TFf0tB+H4QW75fgBffueEyQSX+Gcw13kVdUFD8YweScSQsQSERDv60ERERES6PLsWAp577jleeuklrr76alvbJZdcwogRI7jtttu6fSFAUwNERJrn5uzE4EGDYdBgW5vVYuFIxj7y0jZSc2Q7HiQRlO9KQVkNEaXbGeXyLZR9C4eANVCIP9keA6kKiKNq+LX0HTCUqACtOyAiIie4urry2muv2R5315gibWXXqQH+/v5s2rSJAQMGNGhPS0tj7NixFBcX2ytUp6apASIibZNfVk3m7p+o3bca18JdBJWnEWU9itn0v4+sC2seZY8Rg4+bM3N77eBcl72Yw+MJ6DeaqEGjcHHzcOARiIiIiHQsh00NmDNnDs899xxPPvlkg/YXX3yR3/zmN/YMJSIi3ViwjzvB4ybCuIm2tvKyErL2bKYkfQum3J24mQbjmldDWU090YXfM9b5Oyj4D6RAneHEIecoinwGYw0ZjtPoOQyKicDbTesOiIiIiNh1RMBtt93G66+/TlRUFOPGjQNgw4YNZGZmct111+Hi4mLr+8tiQXeiEQEiIh2jtt7KwYJyjm37FKeMNXgX7yW6dj9+VNj6WA0Tw2teptLkQWxvL270/IEBnmV4RI8ifEgSgaExoKkFIiLdQn19PV9++SUA06ZNw9m5/QvAjogp0pTWfA+1ayHg57f6O2VQk4lvvvnGXmE7jZ+vEZCWlqZCgIiIAxhWKzlZB8jdt5HqrO3UHs/hvtobyC2tBmCF60Mkmffa+h/Dj6PuA6gMiMM1ciS9Eq8gprc3ZrOKAyIiXY3uGiA9mcMKAXKCRgSIiHQ+x8prSM0pxdj0Kh45Gwmq2EeU5QhOP1t3IN/wZ2zNs3i7OTMkzIfrXL4hOMCfgH5jiBk0Elc3NwcegYiInI4KAdKTOWyNABERkc6qt7cb5wwIggH32toqK0rJ3LOZ4kNbMOXuIK/ShJvFTHlNPZsyjvOM20uEZBVDCtQYLux3jqHIdwhG6Aj8+icRM/wsPF31USoiIiJdi11HBFRXV/P000/z7bffkp+fj9VqbbB969at9grVqWlEgIhI11VvsXKosILUI4WEbXgU3+JUImsP4ENVg34brYOYXbeYPoFeDAv343JWExA5kKi4cfj3DnZQ9iIiPZtGBEhP5rARATfeeCNfffUVs2bNYuzYsbqvs4iIdDnOTmYGhvgwMMQHRr8IgGG1kJ2xj7y0DdRkbcezKJWUmliMWjhUUEFuwTGWuf0V8z4DVkO2KZhcj4HUBA7DPXok4XHjCQ6L1ueiiIiIdAp2HRHg5+fHZ599xllnnWWvXXYpWixQRKRnKSirYXd2CRnpB4jf+QghlWmEG3mN+r1bfy6Pud1OXLgvw0O9mGzaSMjAsUT0GYLZyckBmYuIdE8aESA9mcMWC4yLi+Ptt99mxIgR9tpll6SpASIiPVdpcQFHUjdQlr4Vp/wdBJbt49XaX/F6/VQABpsy+cLtPgDKDQ8yXftR6j8Ec3g8vfuPJXpQAi6uWpRQRORMqBAgPZnDpgY88cQT3HvvvTz//PPExMTYc9ciIiJdgq9/EHETLoYJF9va/lhn4fLcMnZll1C6v5S0jIHE1KXjbaoirm4XFOyCgnchBZ60zObb4OsYGu5LQrCJBI98YoaMxcPLx4FHJSLSNbi6uvLMM8/YHnfXmCJtZdcRAQUFBVx55ZWsXbsWT09PXFxcGmwvKiqyV6hOTSMCRETkdOpra8jan8KxA5uoP5qCT3Eq0bUHuK32NtZYEwCYZt7IC67LsBgmspwiKfQeRH3wcLxjRxEVNx6/gCDHHoSIiIh0Gg6bGjBlyhQyMzO58cYbCQkJabQo0ty5c+0VqlNTIUBERM6EYbWQdaySXbnl7M4uwT/tP1x67CUCKW6y/32u93E8airDwv1ICKxncKA7gWExmMzmjk1cREREHM5hhQBPT0/Wr19PfHy8vXbZpWixQBERaQ+F2Yc5uncDVYe34Fq4m9D/Lko4seYJMowwAG5x+phFLv/mGH4cdR9AZUAcrpEjCRk0lvA+QzCZtSihiHR/FouF77//HoBzzjkHpw5YkNURMUWa4rBCwKhRo3j22WcZN26cvXbZJWlEgIiItLeSogJ2Fxmk5pSzO7uUCQef4rKaD3EyNf5YLzc8uD/4H/SOHsbQcF+GB1rpGxaCsxYlFJFuRosFSk/msMUCH3vsMf7whz/w8MMPM3z48EZrBOhLsYiIiH34BQQxIQAm9A/+b8s/qaooI3PPJooPbYHcHQSU7iG6LgM3avk8y5XarHQAHnF+iRinH8hwiaHIdwhG6Ah69RtD9JBE3D21KKGIiEh3Z9cRAeb/zkn85doAhmFgMpmwWCz2CtWpaUSAiIh0FnW1NWQe3MO2ykB2Z5ewO7uUu7MXkmhKbdT3xKKEUSQPeIVBkYEMDfcjLsQdP2/9dUtEugaNCJCezGEjAr799ttmt+3cudOeoURERKQFXFzd6DckgX7ArNGRAFgtP3AkYy95aRupzdqGZ1EqEdX7CTQVY66v4t2UAkgpAOAtl7/Sx7mQXM9B1ASNwCNmFOFxSQSFRDnwqERERKQt7Doi4JfKysr497//zcsvv8yWLVs0IkBERKSTMgyDguxMMjIO8FN1NLuOlrD7aAmfVs/B31TRqH8evdnvPYaNI/7C0Ag/hkX4Ee7n3mhUoIhIR9KIAOnJHDYi4KS1a9fyyiuv8N577xEeHs5ll11GcnJye4QSEREROzCZTARHxBAcEcPYn7WXFKWwK/UnyjK24JK/k+DyPURasgkxHWNfSTb/+OaAre+n7n+m3j2Ait7DcI0cRejgJCJiBuh2hiIiIp2M3QoBubm5LF++nFdeeYXS0lKuvPJKampqWLlyJXFxcfYKIyIiIh3ILyAIv7NnwNkzbG2VZcfJ3LOJisIKrqyMZNfRUgrzjjKUg1B9EI5ugqOvwQY4jg9ZbgM4Gvorakdez9BwP/oEeuFk1sgBERERR7FLIWDGjBmsXbuWiy66iGXLlnHBBRfg5OTE888/b4/ddxnJyckkJyf3mCkQIiLSM3n69GLw2PMZDFz437aammoO7Ari+MHNmHJTCChJJao+k16mMnrVbCXlYAB/3jcSgF6uFv7p/gRl/nE4RyYQNHAs0f2H4+zcLgMVRaQHcXFx4e9//7vtcXeNKdJWdlkjwNnZmdtvv53f//73DBgwwNbu4uJCSkpKjxsRoDUCREREoK6mkqy9Wyg6sJGU6lA+K+1DanYpg+v38oHb4gZ9Kwx3Mlz7UeIXR2X/iwkdPokBId64OTs5KHsREZGupcPXCPjhhx945ZVXGD16NEOGDGHOnDlcddVV9ti1iIiIdFEubp70jT+HvvHnMAa4EbBYDQ4fHsTWFDOWo9vxK95NVO0hvEzVDK3bDYW7+UuOG6+sccHFycS5QRXcaP4MwuMJ6DeG2CFjcHd3d/ShiYiIdGl2vWtARUUFK1as4NVXX2Xjxo1YLBaefPJJbrjhBnx8fOwVptPTiAAREZGWs9bXkX1wJwVpP1F/dDsrrefxSUEQJVV1zDT/wDLXZ219awxnDjvHcsxnCNaweLyHXUj//oPxctO0AhEBi8XC1q1bARg1ahROTu0/qsgRMUWa0prvoe12+8B9+/bxyiuv8MYbb1BcXMzUqVP56KOP2iNUp6NCgIiISNsYhsGR41Vk7V6HS+oHeBXtJqomDR8qG/T7be0fWG2Mpm+gF9N65zPB9RC+fUcTE5eEnz6DRXoc3T5QerJOUQg4yWKx8PHHH/Pqq6+qECAiIiJnzLBaKcxKI2fvT9RkbcXj2C4W1f+OnWUn/tN9h9N7/D+X9wCoN8xkmiPJ9x5MXcgIfGLHED38LAL89Lks0p2pECA9WacqBPREKgSIiIh0nIKyGnZnl1C7bQXhmR8RXrmPAEoa9ZtS83eq/AYwNNyXSb7ZDOplEDVkHEHBIQ7IWkTagwoB0pN1+GKBIiIiIo4S5OPGxEHBMOg24DYwDEryszi6Zz2VGVtwLdiJf2UGh4xwrMVVHC2u4gKXFxjl9AN8C1mEkus5kOrA4XjEjiZiyDhCQ8MxmUyOPjQREZF2oUKAiIiIdC8mE34h0fiFRAOzbc0p1XWkZpeyK7uUXlvDyTkeTJiRTxS5RFXmQuZayIT678yc5fQGA6OCGR7hxzifAvrHRhMcGqXigIiIdAsqBIiIiEiP4OPuQlLf3iT17Q1nvwRAZUkBR1J/oix9M065Owgq30N5vTPZlSay9xWwZl8Bk1wfIMR8gDwCyPYYRHXgMDxiTowcCAqPBRUHRESki1EhwI6Sk5NJTk7GYrE4OhURERFpAU+/IAaOnwHjZ9jaqqur+CC/ml1HS9iRVYzPPgNrvYkQUxEhVeshaz1kvQQ/wB5TPx6PeYHhkX4Mj/BjREAdQcERKg6IiEinpkKAHc2fP5/58+fbFmkQERGRrsfd3YOR0R6MjO4F4wG2Ul1RwuHdGyg+uAlz3g4CS/cQbckk09KL1XvzWb03HzDY4vY7SkwGWR6DqQwcgUfMGMLjJtA7LFbFAZEO4OLiwuLFi22Pu2tMkbbSXQPage4aICIi0v1VVpRyIPMoW4vc2XG0hCNZGfyr9AZcTY1HBh7Dnw3+F3Jw+B8Y9t/RA4Hebg7IWkREuivdNUBERESknXl6+TJiiC8jbC0JVFZO5UDqJo4f2IhTznYCy1KJtRymt6mYrMJSnliVBoAvFXztfi/ZnoOpDhqBV59EIodOoFdQuKMOR0REehCNCGgHGhEgIiIiJ5WXl3E4dSOpx51Yd9yPnUdLCDm2kbdcH27UN5cgsr0Gc7TPFfiNmM7wCD96ebk6IGuRrslqtbJnzx4AhgwZgtls7pYxRZrSmu+hKgS0AxUCRERE5FTKykrI3LWe0kObcMnbTnDZHqKNo7btf6y7kbcsvwLgHL8C7nT5gNrgeLz7JBI9bAJ+vXo7KnWRTq2iogJvb28AysvL8fLy6pYxRZqiqQEiIiIinZiPjx9Dx18A4y+wtZUWHyNr90+UpW/CzTKa2HxPMo5VElG+k9Eua6B8DRwCVsNhUwR53kOwhCbgMWImAwcNwdNV/60TEZGW0SeGiIiISCfg69+boWddBGddxDhgMVBSVceh3b3YkOqBa34KoRV7CDMKiDGOElN2FMq+5je7XVhvZDIg2IeLAo4y3j2DXgPGER2XhKu7p6MPS0REOiEVAkREREQ6KT8PF0aOmQBjJtjaSgtzyNz9I+Xpm3DPT6HAZTDWMtiXV8alxz4l0flj2AN1HzpxwKUPRf7DMUeOJmjwBKL6x2N21n//RER6Oq0R0A60RoCIiIh0pPzSalKOlFC39S3Cj3xGdPVeAiht1G+a8Qy9IvoTH+XPBL/jDIwIJDSqPyYtbibdhNYIkJ5MawSIiIiI9CDBvu5MjXOHuP8H/D8Mq5Ujh9PISf2Buswt+BftwLc2j301veBQET8dKiLB5SnCnDZRiD9ZHkOoCorHs89YooedRUBQqKMPSURE2lGPLgRkZWUxZ84c8vPzcXZ25s9//jNXXHGFbXtsbCy+vr6YzWZ69erFt99+68BsRURERFrGZDYT2WcwkX0GA78FoL7ewucFFew4UkzKkRJ6p0JdrROBpmICq9ZD5nrIfB6+g0OmKJ7st5zhUb0YEenP8AgfvN11G0MRke6iRxcCnJ2dWbZsGQkJCeTm5jJ69GimT5/eYDjPjz/+aBvqIyIiItJVOTs7MSTMlyFhvsxOBC79murKcg6l/kTx/p9wztlGSNluIo0cSi2ufLIrj0925QHwoeuf8HA2ccxvGESMImjwBGIHjcTZxcWxByXyCy4uLtx11122x901pkhbaY2An4mPj+eTTz4hKioKODEiYNeuXa0uBGiNABEREemqSo/nc/BQOj+VBZKSVcy+rDxW1fwGZ5O1Qb8Kw40Mt4HkhkykLmk+CVG9CPVzd1DWIiLSbdYIWLt2LUuXLmXLli3k5OTwwQcfMHPmzAZ9kpOTWbp0Kbm5ucTHx/P0008zduzYVsfasmULFovFVgQAMJlMnHfeeZjNZu68805+85vftPWQRERERDo1317BjBwdzMiTDYbBsezNHN31A9WHN+JduIOYmjS8TNUMrd3J/gwf7ty/FYAwH1eednsGa9BQfPqPJ2bE2Xj69HLYsYiISNM6dSGgoqKC+Ph4brjhBi677LJG21esWMHChQt5/vnnSUpKYtmyZUybNo19+/YRHBwMQEJCAvX19Y1+96uvviI8PByAoqIirrvuOl566aUGfX744QciIiLIyclhypQpDB8+nBEjRrTDkYqIiIh0UiYTvSP60TuiHzAXAGt9PYf3bydvz48UlPsy5Lgv+3JL8So/xJi6NVC+BtKTsXxl4pBzDIV+IyBqLMHDf0V03yGYzSZHHpF0Y1arlczMTACio6Mxd8AdMRwRU6StuszUAJPJ1GhEQFJSEomJiTzzzDPAiX+EUVFR3Hbbbdx3330t2m9NTQ1Tp07lpptuYs6cOc32u/vuuxk6dCjz5s1rch81NTW256WlpURFRWlqgIiIiPQYFTX17D1wgOpt7+CWs4WIil2EUdigzz/qZ/KS8zXER/ozNsyZczzSiRlxDgGBIQ7KWrob3T5QerJuMzXgVGpra9myZQuLFi2ytZnNZqZMmcL69etbtA/DMJg3bx6TJ09uVASoqKjAarXi4+NDeXk533zzDVdeeWWT+3n00Ud58MEHz/xgRERERLo4LzdnRg8dDEMfsLXlHU3n6M611GRswO9YCimWIZRV1/PDgUJcD23ldtfHYS1kmCLJ9R2OETGGwMFn02fIaC1EKCLSjrpsIaCwsBCLxUJISMMKckhICHv37m3RPtatW8eKFSsYMWIEK1euBOCNN95g+PDh5OXlcemllwJgsVi46aabSExMbHI/ixYtYuHChbbnJ0cEiIiIiPRkIRF9CInow8kpBS9YrOzLK2NbZjHWXfs5ejSUCCOXWOMIsSVHoORzSIUKw53He/8Jp0FTGR3Ti5GR/vTydnPswYiIdCNdthBgD2effTZWq7XJbX379iUlJaVF+3Fzc8PNTR9OIiIiIqfi7GRmaLgfQ8P9YNydwJ2UHsshc8daKg79hHf+VmKr9+JlqmZVjhsHsw8CMNfpS252/ZI83+FYIscSNPQ8ogeOxuzco/8rKyJyxrrsu2dgYCBOTk7k5eU1aM/LyyM0NNQhOSUnJ5OcnIzFYnFIfBEREZGuxrd3GMMmzYZJs4ETCxFmpG3ht2UhbMksYWvmcUYV7yfCyCWiJBdKVsHuhynDg3T3oZQHj4GkmxneLxofd00nEBFpiS5bCHB1dWX06NGsXr3atoCg1Wpl9erVLFiwwCE5zZ8/n/nz59sWaRARERGR1jE7OxMbl0QscHXSibbiY0PZvmMtFQfX41OwhX7VqfiYqhhRvZn6w1sZnjaWatNeBoX4cI3/bgYFuRMZP5nwiGhMJt2hQETklzp1IaC8vJwDBw7Ynqenp7N9+3YCAgKIjo5m4cKFzJ07lzFjxjB27FiWLVtGRUUF119/vQOzFhERERF78u8dRMKky2HS5QDU19VyIHUTRXu+oyQ/i95VvThyvIq9uWWMKHqRhIxDsAmyCCPbdwTWyCSCh00kdtBInJx0azcRkU5dCNi8eTOTJk2yPT+5IN/cuXNZvnw5s2fPpqCggAceeIDc3FwSEhL44osvGi0g2FE0NUBERESk/Tm7uNI//iyIPwuAqUB+aTVbDxdR+/0YMo5Zia4/TJQph6jSHEj9ElIfYj9RPBzzKomxASTGBjAi3Bt3N1fHHozYlbOzM7feeqvtcXeNKdJWJsMwDEcn0d205v6NIiIiImJ/VaVFpG//hvL96/DJ30xs9V5WW0eyoO6O//Yw+NHtdkpcQygJHI1Hv7PoM3Iyvr0d8wclEZG2as33UBUC2oEKASIiIiKdS31tNfszj/JTnplNGUXkHNrNB5bbGvU7bI4it9do6gfPoN/Yiwn1c3dAtiIiradCgIP8fGpAWlqaCgEiIiIinZRhtXL00G6yd66BzJ8IKd5OjHHEtv35+ot5rP4aYnt7ck60BzM8dxA96nxCI2IdlbK0gGEYFBYWAifuMtYRi0U6IqZIU1QIcDCNCBARERHpeo7lZ3N42zfUHfyO96vH8G5BJFYDzjWn8Lrr3wDINIWT7T8ac+zZRI0+n7DIvg7OWn6uoqICb29v4MTC415eXt0ypkhTVAhwMBUCRERERLq+0uo6tmQcp2DrR4w++Cx96g9hNjX8r3OWKYxPo+8hcMT5JPUJICrA00HZCqgQID1ba76HallLEREREZEm+Lq7MGlwMAz+LfBbyooLSN/6NVVp39G7cBN96g4SRQ7vpdWxf18KAL/x2cYlXrsxx55DzJhpBEf2c+xBiIg0QYUAO9LtA0VERES6Lx//IEZMvhomXw1AeUkRh7Z+zZTq4XinF7HjSAmJ1etIqvsRtn8G2xeRZQonu/c4XAdMom/ihfgFBDn4KERENDWgXWhqgIiIiEjPU1FTz4GNn1OV+iUBBRvoV7cfp59NJagznJjT+21GDYzmrP6BjI72x91Vf5ezJ00NkJ5MUwNERERERDqYl5sz8efMgHNmAFBSVMjBzV9Qm/YNYUUbKKl34afsOn7KPsizaw7yL9dH8fdwoiz8bAKGT6P/iAk4Oeu/5yLS/jQioB1oRICIiIiI/FJO4THWHa7ixwOFbD2QxaraubiY/jeltBQvDniPxtJnEtFJlxAS2d+B2XZNGhEgPZlGBIiIiIiIdDJhgb2ZFQizRkdiWEeQmfY1udu/xC1zLf0qt+FLBaPK18LOtaza/jFLA5Zw3sAgzh0YRGKkF+4euiPB6Tg7OzN37lzb4+4aU6StNCLAjn6+WGBaWppGBIiIiIhIi9TX1XIwZS1FKV/SK+d7/lU1nn9ZpgAQacpnles97PeIpypmImGjLyaq/whMZrODsxaRzqQ1IwJUCGgHmhogIiIiIm1RXFnLDwcK+W5fAX573+ZPlmcbbM82BZMVMAH3oRcyaNzFuHt6OyhTEeksVAhwMBUCRERERMReDKuV9L2byd/6KV5Z3zGoeieupnrb9jssd1LR/2J+NSSEyYMCCfHruVMIDMOgsrISAE9PT0wmU7eMKdIUFQIcTIUAEREREWkvleUl7N/4OVWpXxBZuJ4Lq/9KGSe+/N/u9D4z3bdQEDaJ3qNm0HfEuZh70Lx1LRYoPZkWCxQRERER6aY8vf2In3wVTL4KwzBYkVPGN3vz+HpPPpPzttHXkk7fI+lw5FWKPvLlYK9zcBr6a4ZMmIGHZ88dLSAi/6MRAe1AIwJERERExBEK845ycP1KnPZ/xcDyjfiaKm3bco0AlvRbwfnDwvnV4BD8PF0cmGn70IgA6ck0IsBBfn7XABERERGRjhYYEkHgzPnAfGpqqtmx4Uuqd35In4Jv2WgdxBepBXyRWoCzGV7s9S88+o6j/zlXEBQc7ujURaQDaURAO9CIABERERHpTAyrhb2Hj/L5/iq+2J2Lkb+XVW73AFBvmNnjNpyy2Avoc/aVhEX3d3C2Z04jAqQn02KBDqZCgIiIiIh0ZoczDpC35mUCj3xJ3/pDDbalOQ8ifdjtJEyeRYivu4MyPDMqBEhPpkKAg6kQICIiIiJdRd7hvWT+sAKfjC8YWLsHs8lgXu3dfGeMZGxsALMGu/GrIaEEdIHpAyoESE+mNQJERERERKRFQmIGExKzGFhMQfZh9q99m4pj4zCyytmQXsSkrH/j+82npHiMonrQpcRNvgYfvwBHp90kJycnZs2aZXvcXWOKtJVGBLQDjQgQERERka7uyPFKPtuZw/Dv5zO+br2tvcZwIdX3LJxHXUvcOTNxcu5+dx8Q6Yo0NcDBVAgQERERke7kyP4dHP3hX4RmfUqM9Yit/QDRvJO4gstGRzI4VP/vFXEkFQIc5Oe3D0xLS1MhQERERES6FcNqJW3HeorWvcbggi/4uH4cD9RfD0BcqA/3ROxg+OSr6N070MGZivQ8KgQ4mEYEiIiIiEh3V1tTzQ+pGbyzq4LVe/NIsO7hXbeHqDJc2el7Lp5jryNuwsWYO3DevBYLlJ5MiwWKiIiIiEi7cnVzZ/LIwUweCccratnyTTGZ26OJtmQytuxrWP01ud8EkRVzOf0umE9AaLSjUxaR/zI7OgEREREREenaenm5MmXGNUT/aQeHZn7Ext4zKTU8CTUKSMx4Hp/nEnj0tXf48WAhGpAs4ngaESAiIiIiIvZhMtE34Tz6JpxHZUUZ61f9C79dr+NRe4wX93nwwr4N9A304s64ciafey7e3ppGK+IIGhEgIiIiIiJ25+nlw/iZvyfuT+upmreKq5Ni8XJ14mjhcc7a8DusSwez/rnfkXNot6NTFelxtFhgO9BigSIiIiIijZXX1LP6++9JXPc7wo1cW/t2z7NwmfgH4hInYzKZznj/WixQerLWfA/ViAAREREREekQ3m7O/HrKJEL/lErKeS+R4p4IQELlOoZ+dhm7Hj6Hdd9/g9Wqv1WKtCetESAiIiIiIh3K7ORE/KQrYdKVZOzdSuGXSxlR9CXD63cy9bP9GJuc+P15/bgkIRwXp5b/7dLJyYnp06fbHncER8QUaStNDWgHmhogIiIiItI6x3PS2bDqXe4+FE9ZdT0AD3q9x6CBg0m4ZD7uHhpyL3IqrfkeqkKAHSUnJ5OcnIzFYiEtLU2FABERERGRViqrruNfP2Xy6fcb+KB+AS4mC/kEkDFsPiMvuQ0XVzdHpyjSKakQ4GAaESAiIiIi0jbVlWXs+Ohpove+QiiFABw1hZCd8P8YddFNODlrlrPIz2mxQBERERER6dLcPX0Ye9Uf8b93J+sH3ssx/Igw8kjcdh9ZjyTw/Q9r+OXfNCsqKvDy8sLLy4uKiooOydMRMUXaSoUAERERERHptNw9PBl/zR/xuGsn6/ssoAQvAiyFLPgkj8uf+5Ftmccb9K+srKSysrJDc3RETJG2UCFAREREREQ6PU9vP8bPfRjTnTv4YvhT1Ln6sTWzmEufXcfHyXeTm3XA0SmKdBmaWCMiIiIiIl2Gr38gV866mvPOr2bpl/s4tu1jZhS8SNXLy1kfcrWj0xPpElQIEBERERGRLifE153Hr4hn/8BKUj/5nLi6XSQdWe7otES6BE0NEBERERGRLmtA/HiGLPqebUnLyCPQ1p7yf7MozE53YGYinZcKASIiIiIi0qWZzGZGXng93rf/YGuLL19H6YsX8faG9EZ3FxDp6TQ1QEREREREugUfv16cd955VFWUkubizN9rZ7H+g1S+P3Ccxy4fjo+7i91jms1mzjvvPNtjka7AZKg8ZnelpaX4+flRUlKCr6+vo9MREREREelxLBYrr67L4G9f7KXeajArJIcHrrsE397Bjk5NpF205nuoSlYiIiIiItLtODmZuencvrzzu/Gc65nBkuL7Ofr8TOpraxydmojD9ehCQFZWFhMnTiQuLo4RI0bw7rvvNtienp7OpEmTiIuLY/jw4VRUVDgoUxEREREROROjonvx4KwkDEwMqdvNtuULHZ2SiMP16EKAs7Mzy5YtIzU1la+++oo777yzwZf9efPm8dBDD5Gamsp3332Hm5ubA7MVEREREZFTqaioICgoiKCgoAb/r+8TN4Y94/4OwKijb3Jgx4/tHlOkM+vRhYCwsDASEhIACA0NJTAwkKKiIgB2796Ni4sL55xzDgABAQE4O2ttRRERERGRzqywsJDCwsJG7WMvvI6tPpNwMhnUfXwXhtXa7jFFOqtOXQhYu3YtM2bMIDw8HJPJxMqVKxv1SU5OJjY2Fnd3d5KSkti4ceMZxdqyZQsWi4WoqCgA9u/fj7e3NzNmzGDUqFE88sgjbTkUERERERFxsPDZT1JtuDCkbje7fvjY0emIOEynLgRUVFQQHx9PcnJyk9tXrFjBwoULWbx4MVu3biU+Pp5p06aRn59v65OQkMCwYcMa/WRnZ9v6FBUVcd111/Hiiy/a2urr6/n+++959tlnWb9+PatWrWLVqlXtd7AiIiIiItKuQiP7sj14JgAuP/zNscmIOFCnHut+4YUXcuGFFza7/cknn+Smm27i+uuvB+D555/n008/5dVXX+W+++4DYPv27aeMUVNTw8yZM7nvvvuYMGGCrT0iIoIxY8bYRghMnz6d7du3M3Xq1Cb3UVPzv9VHS0tLW3yMIiIiIiLScWJ+fT/HXvqSnyojqE7PJaFPqKNTEulwnXpEwKnU1tayZcsWpkyZYmszm81MmTKF9evXt2gfhmEwb948Jk+ezJw5cxpsS0xMJD8/n+PHj2O1Wlm7di1Dhgxpcj+PPvoofn5+tp+TxQMREREREelcwiL78FjcSpbUz+P1TbmOTkfEIbpsIaCwsBCLxUJISEiD9pCQEHJzW/YPet26daxYsYKVK1eSkJBAQkICO3fuBE7cUeCRRx7h3HPPZcSIEQwYMICLL764yf0sWrSIkpIS209WVlbbDk5ERERERNrN1eP7AvDZzhxKquocnI1Ix+vUUwPa29lnn431FKuFnm5qwklubm64ubmRnJxMcnIyFovFnmmKiIiIiEgLmM1mxowZY3vcnJFR/gwK9sarYCvrv63igumXtXtMkc6kyxYCAgMDcXJyIi8vr0F7Xl4eoaGOmeczf/585s+fT2lpKX5+fg7JQURERESkp/Lw8GDTpk2n7WcymXgg/CfOKn2E1G1DoQ2FgJbGFOlMumzJytXVldGjR7N69Wpbm9VqZfXq1YwfP96BmYmIiIiISGc38JwrsRom4up2k5u539HpiHSoTl0IKC8vZ/v27baV/9PT09m+fTuZmZkALFy4kJdeeol//vOf7Nmzh9///vdUVFTY7iLQ0ZKTk4mLiyMxMdEh8UVEREREpGWCIvqQ6jYcgMPfveHgbEQ6lskwDMPRSTRnzZo1TJo0qVH73LlzWb58OQDPPPMMS5cuJTc3l4SEBP7xj3+QlJTUwZk2dHJqQElJCb6+vg7NRURERESkp6isrCQuLg6A1NRUPD09T9n/p3eWMi71r6Q5D2Tgn85seH9rY4q0l9Z8D+3UhYCuSoUAEREREZGOV1FRgbe3N3BidLGXl9cp+x/LyaT3CydGBeTfvJPg8Oh2jynSXlrzPbRTTw0QERERERFpL73DotnvPACAjJ8+cHA2Ih1HhQA70hoBIiIiIiJdS2H4ZACsh9Y6OBORjtNlbx/YGen2gSIiIiIiXYv/hHlcuj+cg8YgNtdbcXXW30ql+9NVLiIiIiIiPdaggUPI8hpKaY2VzRlFjk5HpEOoECAiIiIiIj2W2Wzi3AFBAPxwoNDB2Yh0DE0NsKPk5GSSk5OxWCyOTkVEREREpMcxmUy2W/mZTKYW/96U0ArGOr9I+Hbggg87JKaII+n2ge1Atw8UEREREek6jh7aQ8Tr46g1nKi/Ox1Pb633JV2Pbh8oIiIiIiLSQuGxg8ghCFeThUNbv3F0OiLtToUAERERERHp0UxmM5l+owEo37fGscmIdAAVAuwoOTmZuLg4EhMTHZ2KiIiIiEiPU1lZydChQxk6dCiVlZWt+l1TVBIAXgXbOyymiKNojYB2oDUCREREREQ6XkVFBd7e3gCUl5fj5eXV4t/N3LOJ6BVTqDDccfvTEZxdXNo9pog9aY0AERERERGRVogcMJIKwx0vUzWH9211dDoi7UqFABERERER6fHMzs6kuw9mvzWCjKwjjk5HpF2pECAiIiIiIgJ8NiKZqbVL+bpqoKNTEWlXKgSIiIiIiIgAw6N7A5CSVezYRETamQoBdqS7BoiIiIiIdF3xUf4ApOWVUF1b59hkRNqRCgF2NH/+fFJTU9m0aZOjUxERERER6XFMJhMxMTHExMRgMpla/fthfu78y+NxtrvcyOHUjR0SU8QRnB2dgIiIiIiIiD14enqSkZFxxr9vMpno7VKPd201xYe2QMJZ7R5TxBE0IkBEREREROS/Sv0HA2Dk7nJwJiLtR4UAERERERGR/zKFDgfAp3ivgzMRaT8qBIiIiIiISLdQVVVFYmIiiYmJVFVVndE+/PuMBCCi9iAYRofEFOloWiNARERERES6BavVyubNm22Pz0TkoJFYDRP+pnKO5R+hd0hUu8cU6WgaEWBHun2giIiIiEjX5unpTY45GIDsAzscnI1I+1AhwI50+0ARERERka7vgPcYVltGcqTU4uhURNqFCgEiIiIiIiI/s27In7mx7m421vdzdCoi7UKFABERERERkZ/pE+gFQHphhYMzEWkfKgSIiIiIiIj8zIlCgEFhQa6jUxFpF7prgIiIiIiIdBuBgYFt3kc/7zq2ud2Cb0UFtdXZuLp7tntMkY6kQoCIiIiIiHQLXl5eFBQUtHk/gYHBlGPByWRwJGMfMYNHtntMkY6kqQEiIiIiIiI/YzKbKXAOBeDY0f0OzkbE/lQIEBERERER+YUy93AAagoOOTgTEftTIUBERERERLqFqqoqJk6cyMSJE6mqqmrTvmq8IwAwjmd2WEyRjqI1AkREREREpFuwWq189913tsdt4h8LeeBWntVxMUU6iEYE2FFycjJxcXEkJiY6OhUREREREWkDt8BYAHyrcxybiEg7UCHAjubPn09qaiqbNm1ydCoiIiIiItIG3pFxfGNJ4HvLUEenImJ3mhogIiIiIiLyC0F9hvGrunugDmbX1OPlpq9O0n1oRICIiIiIiMgv+Lq74Ot+4sv/0WItAijdiwoBIiIiIiIiTYjw98CHSnIKjjk6FRG7UiFARERERES6DU9PTzw9Pe2yr4drH2On+29x3ftBh8UU6Qia6CIiIiIiIt2Cl5cXFRUVdtufxT0AKsFa2vydA+wdU6QjaESAiIiIiIhIE6zeoQCYy3MdnImIfakQICIiIiIi0gQnv3AA3KvyHJyJiH2pECAiIiIiIt1CdXU1F110ERdddBHV1dVt3p9brxOFAK/awg6LKdIRtEaAiIiIiIh0CxaLhc8++8z2uK28A6MA6GVp/q4B9o4p0hF69IiArKwsJk6cSFxcHCNGjODdd9+1bdu3bx8JCQm2Hw8PD1auXOm4ZEVEREREpEP1Co0BIMAopqa2xsHZiNhPjx4R4OzszLJly0hISCA3N5fRo0czffp0vLy8GDRoENu3bwegvLyc2NhYpk6d6tiERURERESkw/gFhvG1dQy5Vj8mFpUSGRrk6JRE7KJHFwLCwsIICwsDIDQ0lMDAQIqKivDy8mrQ76OPPuJXv/pVo3YREREREem+TGYnFnvez9HiKj6odSbS0QmJ2Emnnhqwdu1aZsyYQXh4OCaTqcmh+cnJycTGxuLu7k5SUhIbN248o1hbtmzBYrEQFRXVaNs777zD7Nmzz2i/IiIiIiLSdQV6uwJwrLzWwZmI2E+nLgRUVFQQHx9PcnJyk9tXrFjBwoULWbx4MVu3biU+Pp5p06aRn59v65OQkMCwYcMa/WRnZ9v6FBUVcd111/Hiiy82ilFaWsqPP/7I9OnT7X+AIiIiIiLSqfX2csWHSkpKihydiojddOqpARdeeCEXXnhhs9uffPJJbrrpJq6//noAnn/+eT799FNeffVV7rvvPgDbPP/m1NTUMHPmTO677z4mTJjQaPuHH37I+eefj7u7+yn3UVPzv8VDSkpKgBNFBBERERER6RgVFRW2x6WlpXZZxf/avL+zzPQVG1J+R+nQP3VITJEzcfL7p2EYp+3bqQsBp1JbW8uWLVtYtGiRrc1sNjNlyhTWr1/fon0YhsG8efOYPHkyc+bMabLPO++8w80333zK/Tz66KM8+OCDjdqbmmYgIiIiIiLtLzw83M57XAq/X9rBMUVar6ysDD8/v1P26bKFgMLCQiwWCyEhIQ3aQ0JC2Lt3b4v2sW7dOlasWMGIESNs6w+88cYbDB8+HDjxl/2NGzfy3nvvnXI/ixYtYuHChbbnVquVoqIievfujclkasVROV5iYiKbNm1ydBoO0xWPv7Pl3NH5dES89ohhz322ZV+lpaVERUWRlZWFr6+vXfKRjtHZ/u13tK56/J0pb0fk0t4xu/P7Neg9u6vqTP/uHaUrvgadKeeW5mIYBmVlZS0qSHXZQoA9nH322Vit1ma3+/n5kZeXd9r9uLm54ebm1qDN39+/rek5hJOTU4/+YOmKx9/Zcu7ofDoiXnvEsOc+7bEvX1/fTnUdyel1tn/7Ha2rHn9nytsRubR3zJ7wfg16z+5qOtO/e0fpiq9BZ8q5NbmcbiTASZ16scBTCQwMxMnJqdEX9by8PEJDQx2UVdc3f/58R6fgUF3x+Dtbzh2dT0fEa48Y9txnZ7sGpGP09PPeVY+/M+XtiFzaO6ber6Uz0nnvmq9BZ8q5PXIxGS1ZSaATMJlMfPDBB8ycOdPWlpSUxNixY3n66aeBE0Pyo6OjWbBggW2xQBERaV5paSl+fn6UlJR0mqq3iIg0Te/ZImIvnXpqQHl5OQcOHLA9T09PZ/v27QQEBBAdHc3ChQuZO3cuY8aMYezYsSxbtoyKigrbXQREROTU3NzcWLx4caPpTSIi0vnoPVtE7KVTjwhYs2YNkyb9f/buOzyKcm/j+Hd3s5tKEgik0SH03oSgIigSFFEEFGyAYOMFFVFEjx7E3rtHwEbQAyoeBUUERAQEQUAQCFVAekgoIY303Xn/CFmJCZBAkkm5P9e1F+zMMzP3lGwyv31mpleB4cOHDyc6OhqA9957j1dffZW4uDjat2/PO++8Q9euXcs4qYiIiIiIiEjFUK4LASIiIiIiIiJSsirszQJFREREREREpPhUCBARERERERGpQlQIEBEREREREalCVAgQERGRKikrK4uIiAhWrVpVovNduHAh7du3x+Vyleh8RURESooKASIiIpXAiBEjsFgsBV5nPoZX8ps6dSoNGzake/fu7mEWi4W5c+cWaDtixAgGDBhQpPn27dsXu93OzJkzSyipiIhIyVIhQEREpJLo27cvR44cyfdq2LBhgXZZWVkmpCtfDMPgvffeY9SoUaUy/xEjRvDOO++UyrxFREQulgoBIiIilYSnpyehoaH5XjabjZ49ezJ27FjGjRtHzZo1iYqKAmDLli1cc801+Pn5ERISwh133MHx48fd8zt16hTDhg3Dz8+PsLAwXn/9dXr27Mm4cePcbQr7Bj0wMJDo6Gj3+4MHD3LzzTcTGBhIjRo1uOGGG9i3b597fN637a+99hphYWEEBQUxZswYsrOz3W0yMzOZOHEidevWxdPTk4iICD7++GMMwyAiIoLXXnstX4aNGzees0fE+vXr2bNnD/369SvmVoZ9+/YV2vuiZ8+e7jb9+/fn999/Z8+ePcWev4iISGlTIUBERKQKmDFjBg6Hg19//ZWpU6eSmJjIlVdeSYcOHfj9999ZuHAh8fHx3Hzzze5pJkyYwPLly/n222/58ccfWbZsGRs2bCjWcrOzs4mKiqJatWqsWLGCX3/9FT8/P/r27ZuvZ8LSpUvZs2cPS5cuZcaMGURHR+crJgwbNozPP/+cd955h+3btzNt2jT8/PywWCyMHDmS6dOn51vu9OnT6dGjBxEREYXmWrFiBU2bNqVatWrFWh+AunXr5ut18ccffxAUFESPHj3cberVq0dISAgrVqwo9vxFRERKm4fZAURERKRkfP/99/j5+bnfX3PNNXz11VcANGnShFdeecU97rnnnqNDhw688MIL7mGffPIJdevW5c8//yQ8PJyPP/6Y//73v1x11VVAbjGhTp06xcr05Zdf4nK5+Oijj7BYLEDuSXpgYCDLli2jT58+AFSvXp333nsPm81G8+bN6devH0uWLOHuu+/mzz//ZPbs2SxevJjevXsD0KhRI/cyRowYwaRJk1i7di2XXHIJ2dnZzJo1q0AvgTPt37+f8PDwQsfdcsst2Gy2fMMyMzPdvQdsNhuhoaEAZGRkMGDAACIjI5k8eXK+acLDw9m/f38xtpaIiEjZUCFARESkkujVqxdTpkxxv/f19XX/v1OnTvnabtq0iaVLl+YrHOTZs2cP6enpZGVl0bVrV/fwGjVq0KxZs2Jl2rRpE7t37y7wzXtGRka+bvOtWrXKd/IdFhZGTEwMkNvN32azccUVVxS6jPDwcPr168cnn3zCJZdcwrx588jMzOSmm246a6709HS8vLwKHffmm2+6Cw55Jk6ciNPpLNB25MiRpKSksHjxYqzW/B0tvb29SUtLO2sGERERs6gQICIiUkn4+vqetSv8mUUBgNTUVPr378/LL79coG1YWFiRnzZgsVgwDCPfsDOv7U9NTaVTp06F3kG/Vq1a7v/b7fYC8817/J63t/d5c9x1113ccccdvPnmm0yfPp0hQ4bg4+Nz1vY1a9Z0Fxr+KTQ0tMB2rFatGomJifmGPffccyxatIi1a9cWeolBQkJCvnUUEREpL1QIEBERqYI6duzI119/TYMGDfDwKPjnQOPGjbHb7axZs4Z69eoBcPLkSf78889838zXqlWLI0eOuN/v2rUr37fgHTt25MsvvyQ4OBh/f/8LytqmTRtcLhfLly8v8E19nmuvvRZfX1+mTJnCwoUL+eWXX845zw4dOjBlyhQMw3BfslAcX3/9Nc888wwLFiygcePGBcbn9Xjo0KFDsectIiJS2nSzQBERkSpozJgxJCQkcMstt7Bu3Tr27NnDokWLuPPOO3E6nfj5+TFq1CgmTJjAzz//zJYtWxgxYkSB7u9XXnkl7733Hn/88Qe///479913X75v92+77TZq1qzJDTfcwIoVK9i7dy/Lli3jgQce4NChQ0XK2qBBA4YPH87IkSOZO3euex6zZ892t7HZbIwYMYLHH3+cJk2aEBkZec559urVi9TUVLZu3VqMrZZry5YtDBs2jIkTJ9KqVSvi4uKIi4sjISHB3ea3337D09PzvDlERETMoEKAiIhIFRQeHs6vv/6K0+mkT58+tGnThnHjxhEYGOg+2X/11Ve5/PLL6d+/P7179+ayyy4rcK+B119/nbp163L55Zdz66238sgjj+Trku/j48Mvv/xCvXr1GDhwIC1atGDUqFFkZGQUq4fAlClTGDx4MP/3f/9H8+bNufvuuzl16lS+NqNGjSIrK4s777zzvPMLCgrixhtvLPSShfP5/fffSUtL47nnniMsLMz9GjhwoLvN559/zm233XbOyxNERETMYjH+eWGfiIiIyFn07NmT9u3b89Zbb5kdpYAVK1Zw1VVXcfDgQUJCQs7bfvPmzVx99dXs2bOn0JsmXqjjx4/TrFkzfv/9dxo2bFhi8xURESkp6hEgIiIiFVpmZiaHDh1i8uTJ3HTTTUUqAgC0bduWl19+mb1795Zonn379vH++++rCCAiIuWWbhYoIiIiFdrnn3/OqFGjaN++PZ9++mmxph0xYkSJ5+ncuTOdO3cu8fmKiIiUFF0aICIiIiIiIlKF6NIAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARERERERGRKkSFABEREREREZEqRIUAERERERERkSpEhQARKTHR0dFYLBb27dtXYvNMTU3FarXyxhtvlNg8i+OVV16hefPmuFwuU5ZvhqlTp1KvXj0yMzPLbJlVcTuXlQvdnxV9n1Tl47iq7vN/qsrHgJjDjGNO5EKpECBSSb3//vtYLBa6du1qdpSLsmXLFgzDoFWrVmW+7OTkZF5++WUmTpyI1Vp1Pi5HjBhBVlYW06ZNK5PlFbadU1NTeeqpp+jbty81atTAYrEQHR1d5Hlu3bqVm266iUaNGuHj40PNmjXp0aMH8+bNK9B2xIgRWCyWs74OHz5c5OWWVe7iZL6Q/VkZjv3ycBwDZGZmMnHiRMLDw/H29qZr164sXrz4gpdTlPmVxj4v698pJbG88nAMFPczoajHS1HbXexn0q5duxg6dCh16tTBx8eH5s2b88wzz5CWllbkeVxIjuL83BSl7cVuByjatijrY07kohgiUil1797daNCggQEYu3btKpNlTp8+3QCMvXv3ltg8P/jgAwMwDhw4UGLzLKo333zT8Pf3N9LT08t82WZ79NFHjfr16xsul6vUl1XYdt67d68BGPXq1TN69uxpAMb06dOLPM/58+cbUVFRxuTJk40PPvjAeOutt4zLL7/cAIxp06bla7tq1Srjs88+y/f69NNPDR8fH6Nly5bFWpeyyl3czMXdn5Xl2Df7ODYMwxg6dKjh4eFhPPLII8a0adOMyMhIw8PDw1ixYsUFLaeo8yvpfd69e3fD4XCU2e+Uklqe2cdAcT8Tirp/i9ruYj6TDhw4YAQGBhr169c3XnzxRWPatGnGiBEjDMC4/vrri7xdSnM7FLXtxX42F2dblOUxJ3IxVAgQqYT++usvAzC++eYbo1atWsbkyZPLZLmlUQi4//77DX9//xKbX3G0bdvWuP32201Zttl+//13AzCWLFlS6ssqbDtnZGQYR44cMQzDMNatW1fsP9oKk5OTY7Rr185o1qzZeduuWLHCAIznn3++WMswM/e5Mhd3f1aWY9/s43jNmjUGYLz66qvuYenp6Ubjxo2NyMjIYi+jOPMryX2e9ztl8uTJht1uL/XfKSW5PLOPgeJ8JhR1/xbnOLiYz6Tnn3/eAIwtW7bkGz5s2DADMBISEoo0n+LmKM76FbXtxX42F2dblOUxJ3IxKmZ/PxE5p5kzZ1K9enX69evH4MGDmTlzZoE2kydPxmKxsHv3bkaMGEFgYCABAQHceeedBbr8LVu2jM6dO+Pl5UXjxo2ZNm2ae/qiOHz4MCNHjiQkJARPT09atWrFJ598UqRpY2JiaNGiBRs2bOCaa66hWrVq1K5dm7fffrtI01+ovXv3snnzZnr37l1g3NVXX01kZCSrV6+mZ8+e+Pr6EhERwQ8//ADADz/8QLdu3fD19aV9+/asX7++xPOVdoZOnTpRo0YNvv322xLPfqazbWdPT09CQ0NLdFk2m426deuSmJh43razZs3CYrFw6623FmsZZuY+V+bi7M+z7ZOPPvoILy8vLr30Uvbv3+8ebhgGvXr1ombNmhw9erRoK3UOJbkcs4/j//3vf9hsNu655x73MC8vL0aNGsXq1as5ePBgsZZTnPmVxD7PM3PmTKxWK/feey+9e/cu9HdKSSrJ5Zl9DBTnM6Go+7c4x8HFfCYlJycDEBISkm94WFgYVqsVh8NR5HmVxnYoTtuL/WwuzrYoq2NO5GKpECBSCc2cOZOBAwficDi45ZZb2LVrF+vWrSu07c0330xKSgovvvgiN998M9HR0Tz99NPu8X/88Qd9+/blxIkTPP3004waNYpnnnmGuXPnFilLfHw83bp146effmLs2LG8/fbbREREMGrUKN56663zTh8TE0NKSgrXXXcdHTt25LXXXiMsLIyHHnqImJiYImW4EKtWrQKgY8eOBcZt3ryZpKQkhgwZQq9evXjhhRdISUnh1ltvZdq0aTzwwAPceOONPPnkk+zZs4eRI0eWeL6yyNCxY0d+/fXXs47Pzs7m+PHjRXqd7cZZ59rOJeHUqVMcP36cPXv28Oabb7JgwQKuuuqqc06TnZ3N7Nmz6d69Ow0aNCiVXOdT3NxFyXy+/ZnnbPukS5cuTJgwgd9++43XXnvNPfw///kPy5Yt49133yU4OLgIa3duJb0cM4/jP/74g6ZNm+Lv759v+CWXXALAxo0bi7weFzK/i93neWbOnMkVV1xBaGgoQ4YMOefvlJJQ0surKJ9lRd2/JX1cnU3Pnj0BGDVqFBs3buTgwYN8+eWXTJkyhQceeABfX98SWc4/FWf9yuu2KOrPnoipzO6SICIlK69L2uLFiw3DMAyXy2XUqVPHePDBB/O1e+qppwzAGDlyZL7hN954oxEUFOR+379/f8PHx8c4fPiwe9iuXbsMDw8P458fIYVdGjBq1CgjLCzMOH78eL62Q4cONQICAoy0tLSzrktsbKwBGDVr1sx3j4Bt27YZgDFjxoxzb4yL8OSTTxqAkZKSkm94fHy8ARghISFGbGyse/g777xjAEbz5s2NpKQk9/Dx48cbFovFyMjIKLFsZZXhnnvuMby9vc86funSpQZQpNfZLhc523Y+08V0sb/33nvdGaxWqzF48ODzdmedN2+eARjvv/9+sZd3prLMXZTM59ufec63T/r06ePucrtnzx7D19fXGDBgwHnnW1wltRwzj+NWrVoZV155ZYH2W7duNQBj6tSpxVqX4s6vJPZ53u+UvHknJiYanp6eBX6nlJTSWF5F+Swr6v690OPqQj6Tnn32WcPb2zvfNnjiiSeKPP2F5CjO+l3ItrjQz+bibIui/uyJmMnjwksIIlIezZw5k5CQEHr16gWAxWJhyJAh/Pe//+X111/HZrPla3/ffffle3/55ZczZ84ckpOT8fX15aeffuLGG28kPDzc3SYiIoJrrrmm0Duwn8kwDL7++mtuvvlmDMPg+PHj7nFRUVF88cUXbNiwgUsvvbTQ6Tdv3gzAM888Q926dd3D7XY7QLG6JRbXiRMn8PDwwM/Pr9BMkydPJiwszD08r92rr76a75uJgIAArFZrid55vawyVK9enfT0dNLS0vDx8Skwvl27dkW++/nZumSebTuXlHHjxjF48GBiY2OZPXs2TqeTrKysc04za9Ys7HY7N998c6lkKori5i5K5vPtzzzn2yft27dnypQpuFwuRo4ciaenJ1OmTCn6yhVRSS3HzOM4PT0dT0/PAu29vLzc44ujuPMriX0+c+ZMPDw8GDRoEJD7edK3b1+++OKLQn+nXKzSWF5F+Swr6v4t6ePqXBo0aECPHj0YNGgQQUFBzJ8/nxdeeIHQ0FDGjh1bYss5U3HWr7xui6L+7ImYSYUAkUrE6XTyxRdf0KtXL/bu3ese3rVrV15//XWWLFlCnz598k1Tr169fO+rV68OwMmTJzl16hTp6elEREQUWFZhw/7p2LFjJCYm8sEHH/DBBx8U2uZc1/rmdf2/8cYb8w3fsWMHAM2aNTtvhpKWl+n666/PN3znzp14e3tz9dVX5xv+559/0rhxY3fxYsqUKXz44YfExMTwxBNPMHny5DLNkJmZyejRo/npp59ITEykZcuWvPnmm0RGRhZYjmEYAGe9F0T16tXPek1xedG8eXOaN28OwLBhw+jTpw/9+/dnzZo1ha5Xamoq3377LVFRUQQFBZV1XLfi5C5q5vPtz6Jq3bo1KSkpTJgwgeXLl/PZZ5/lOzkqiWP8fMupKMext7d3oc8Tz8jIcI8vzfld7D7P+51y5ZVXUrNmTffwIUOG8O233xb4nXKx+744y6sox0BxFHX/lvRxdTZffPEF99xzD3/++Sd16tQBYODAgbhcLiZOnMgtt9xSKp+TxVm/8rotSurzVqQ0qRAgUon8/PPPHDlyhC+++IIvvviiwPiZM2cWKASc7duVvF9iFyPvWsrbb7+d4cOHF9qmbdu2Z50+JiaG2rVrF/gGZtOmTXh4eNCyZctzLn/EiBH07NmTESNGFC84EBQURE5ODikpKVSrVs09fPPmzYSFheXrIZGXqXXr1gW+mdi0aVO+dQwLC2Py5MnMmjWr2JlKIkNOTg4NGjRg5cqV1KlTh9mzZ9O/f3/27dtX4JuskydP4uPjc9Y/pLKyskhISChS5lq1ahV6rJ1tO5eWwYMHc++99/Lnn38WWkiaO3cuaWlp3HbbbaWepTjOlbuomc+3P/Ocb5+0bt0agDfeeIPrrruO22+/Pd/4kjjGz7ecinIch4WFcfjw4QLtjxw5AlDgZ/h8iju/i93neb9TnnvuuXztr7/+enx8fAr8TrnYfV+c5VWUY6A4irp/S/q4Opv333+fDh06uE9881x//fVER0fzxx9/lEoBpTjrV163RVF/9kTMpJsFilQiM2fOJDg4mK+++qrA65ZbbmHOnDnF6iYXHByMl5cXu3fvLjCusGH/VKtWLapVq4bT6aR3796Fvs5106+YmJhCCwWbN2+madOmhXYHLCl538ae2bMib9nt2rUr0H7Tpk0FhmdnZ7Nz58586zBgwACuv/56AgMDLzjbxWTw9fVl0qRJ1KtXD6vVytChQ3E4HOzcubPA/Pbu3UuLFi3OmmPVqlWEhYUV6XW2u6OfbTuXlrzjPykpqdDxM2fOxM/Pr0BvC7OdK3dRM59vf+Y53z7JK0QEBgYybdq0AuNL4hg/33IqynHcvn17/vzzT/cdx/OsWbPGPb44iju/i93nM2fOxG63F+iV5evrS79+/Qr8TrnYfV+c5VWUY6A4irp/S/q4Opv4+HicTmeB4dnZ2UBuMaY0FGf9yuu2KOrPnoiZVAgQqSTS09P55ptvuO666xg8eHCB19ixY0lJSeG7774r8jxtNhu9e/dm7ty5xMbGuofv3r2bBQsWFGn6QYMG8fXXX7Nly5YC448dO3bWaZ1OJ9u3bz/rCe+5ehKUhLzupb///nu+TNu2bSuQ6fjx4xw5cqTA8O3bt5OdnV2iWUs6w65du0hISCj0Uo8NGzbQvXv3s2bJu662KK+zXVdb2HYurrS0NHbs2JHvHhSFXXKSnZ3Np59+ire3d6G9SY4dO+a+J0ZpX9NZWGYofu7iZD7f/sxzvn3y4YcfArnfhJXUt20Xu5zyehwPHjwYp9OZ79KozMxMpk+fTteuXfPd+6Qoiju/i9nneb9T+vTp475k7ExDhgwp9u+Uc7nY5ZXXY6A4irp/S/q4yvPPz6WmTZvyxx9/8Oeff+Zr9/nnn2O1Wkvt93Bx1q80tkVhn8/F3RZF/dkTMZMuDRCpJL777jtSUlLO+q1gt27dqFWrFjNnzmTIkCFFnu/kyZP58ccfufTSSxk9ejROp5P33nuP1q1bF+mxPC+99BJLly6la9eu3H333bRs2ZKEhAQ2bNjATz/9dNbumLt27SIjI6PAL9f09HR279591ksNSkqjRo1o3bo1P/30k/vRe3mZ/nmyvWnTJoACw/Nu6lfcP5YsFgtXXHEFy5YtKzCuJDOkp6dz++238/jjjxMQEJBv3Pr160lISOCGG244a86SuK62sO2c57333iMxMdFdhJo3bx6HDh0C4P7773dnXrt2Lb169eKpp55yX5N87733kpycTI8ePahduzZxcXHMnDmTHTt28Prrrxd6Q68vv/ySnJycc3axP9e+KU7uwjJfSO6iZIai7c8859one/bs4V//+hfw9706LsT5tmNxllOej+OuXbty00038fjjj3P06FEiIiKYMWMG+/bt4+OPPy4wn/Ntl+LM72L3ed7vlLP9vujXrx/VqlUr9u+Us63jxSyvPB8DUPTPsqLu3+IeVxf6WTphwgQWLFjA5ZdfztixYwkKCuL7779nwYIF3HXXXQUKdOc7fkt6OxS37cX8TinOtijOz56IqUx7XoGIlKj+/fsbXl5exqlTp87aZsSIEYbdbjeOHz/ufnzgsWPH8rUp7BGAS5YsMTp06GA4HA6jcePGxkcffWQ8/PDDhpeX13mnNYzcx92NGTPGqFu3rmG3243Q0FDjqquuMj744IOzZp09e7YBGFu2bMk3fO3atQZgfP/994VO169fPyMgIMAICAgw7Ha74e3t7X7/4osvnnV5hXnjjTcMPz8/9yMOz5bpjTfeMAAjMTEx3/BHH33U8Pf3N1wuV4F533vvvcZTTz1VYHhKSooBGEOHDi00U0llyMrKMvr162fceuutheabOHGiUa9evULHlbR/buc89evXL9IjvPIe/XXm9vz888+N3r17GyEhIYaHh4dRvXp1o3fv3sa333571hzdunUzgoODjZycnELHn2/fFCd3YZkvJPf5Mucp7v4sbJ+4XC7jiiuuMKpXr27ceeedhp+f3znnd6HHeHGWUxGO4/T0dOORRx4xQkNDDU9PT6NLly7GwoULC0xf1OOrqPO72H3ev39/w9PTM9+jSP/ptttuc/9OOdOF7PsLXV5FOAaK+llmGEXfv0VtV5zlF/a5tGbNGuOaa64xQkNDDbvdbjRt2tR4/vnnjezs7HzLKMrxWxrboThtL2Y7FGdblOUxJ3IxVAgQkQtyww03GBEREWbHOKfhw4df0DPc8yQmJho1atQwPvroo5ILddrZ/lCeP3++YbFYjM2bN5f4MvM4nU5jyJAhxnXXXVfgDxjDMIyMjAwjNDTUeOutt0otw5lKczuXpLLYN6XhQvZnYfvkvffeMwDj008/NT7//HMDMPbs2XPWeVzoMV7U5VS247gkj6+S2ucXqqw+3yrbMVCRVdTPx5JW1secyMXQPQJE5Lz+eYPBXbt28cMPP9CzZ09zApWRgIAAHn30UV599VX3ExAuVk5ODhkZGTidznz/z7N06VKGDh1KmzZtSmR5hbn33ns5cuQIX331FR4eBa8Qmz59Ona7nfvuu6/UMpypNLZzaSiLfVMaLmR//nOf7Nu3j8cee4z+/ftzxx13uLfBhg0bCkx7Mcd4cZZT2Y7jkjy+SmKfX4iy/nyrbMdARVZRPx9LWlkfcyIXw2IYJfCMMBGp1MLCwhgxYgSNGjVi//79TJkyhczMTP744w+aNGlidryzupjHB5aWyZMn8/TTT+cbNn369DLLuH//fho0aICXl1e+R2DlXfso8k+GYdC7d2/++OMPtm7dSlhYGDk5OVSvXp3w8HAefvhhbrvtNnx9fYELP8aLsxwdx+VTWX6+6RgQEbk4KgSIyHndeeedLF26lLi4ODw9PYmMjOSFF16gY8eOZkcTkVI2bdo07rvvPj799FPuuOMO9/Do6Gj+/e9/c+zYMVJSUrDb7RViOSIiIqJCgIiIiIiIiEiVonsEiIiIiIiIiFQhKgSIiIiIiIiIVCEFb7EqF83lchEbG0u1atWwWCxmxxEREREREZFKzjAMUlJSCA8Px2o993f+KgSUgtjYWOrWrWt2DBEREREREaliDh48SJ06dc7ZRoWAUlCtWjUgdwf4+/ubnEZEREREREQqu+TkZOrWres+Hz0XFQJKQd7lAP7+/ioEiIiIiIiISJkpyuXpulmgiIiIiIiISBWiQoCIiIiIiIhIFaJCgIiIiIiIiEgVonsEmMjpdJKdnW12jErHbrdjs9nMjiEiIiIiIlIuqRBgAsMwiIuLIzEx0ewolVZgYCChoaFFulGGiIiIiIhIVaJCgAnyigDBwcH4+PjoZLUEGYZBWloaR48eBSAsLMzkRCIiIiIiIuWLCgFlzOl0uosAQUFBZseplLy9vQE4evQowcHBukxARERERETkDLpZYBnLuyeAj4+PyUkqt7ztq3swiIiIiIiI5KdCgEl0OUDp0vYVEREREREpnAoBIiIiIiIiIlWICgEiIiIiIiIiVYgKAVJkI0aMYMCAAedsc+jQIRwOB61bty50vMViYe7cuSUfTkRERERERIpETw2QEhUdHc3NN9/ML7/8wpo1a+jatavZkURERKQwhgEuJ7iywZkFzhywe4HDN3d8djqc2A3ObHDl/OPlhOoNoGaT3LYZSbBzQcE2ef+Gt4eGPXLbpp+ElW+eMf4fbRtcCh1uPz3fZJhzHxjO3HGGE8Plwjj93tmgJ9mXPYzLACM7A5/PrgHDwDBckO9lkFWvB8lXvoiBgcuAkE+6nJ5nbhvLGe3Ta3cnNuqD3E1kGER81glr9qnTG+7M+xBZSA3uwO4+M3IzGNBmzlXYM47nbuJ/tD1VvRlbes86Pc6g/cKBeKYeOj06f9t0v3psuPpL95D2S4bhk7wnX4Tc+VvI9K7Fut5fnx4G7X79P6qd3FYgrwHk2Kux6uq57mFt1j5K9RN/YORrm/uv02bnl97z3IdLm41PE3RszRkxLRhYMSw2DIuF5T2+xLDaAWi24z/UOrYaw2LLbWexYWDBsFjBYmNd59dweuQea/X3zabm8XVgtWFY8uZ3+l+sbG/xADkOfwBC4pdTPWFz7jirDYPcf7FYcVk8OFT3enIc/lgs4J+4jWrJe3BZbRgWD7B4YFhs7vdJNdrgtPsB4Mg4hmfGcbDkjjMsVgyrR+7/bTayHYHudbO4cnK3icXDvd8CvO34OAo+qcqSk4HFlVVgeB6Xhw9YPU63TcfiOn2ja4sHLvt5bi5uGFizU9z7zOWodo62LqzZqbn/tXli2DwLbWZxZmJxZgJWXA6/cy7e4szCsDkKDLdmn8rdPx5eBac5vT3OXO98XDkFhudlctn9wPL399i588ousN7WrOQC62jJSQfA8PA+5zAMFxZXzt/r5crBmpOOy1ENq8VCmzoBZ9kaFY8KAVJiDMNg+vTpvP/++9SpU4ePP/5YhQAREZFzcbkgJx2yM8DhA/bTf5CeOgHxMbnD88af+W/E1RDWlhyni+zYzdh+fQsjOx1XdjrkZGLkZGFxZoErmyOt7yWuwQ3kOA1849fRbvldWFzZWF3ZWE6f/uVZ2/h+NtS7kxynixrJ27h147CzRl9U43a+DRpJjtOgVuYBnj9871nbfus9gI98rDhdBjWcx/hv8ttnbfvN5qM8My8Yp8ugmiuFVdb5+cZb+Pt09bu9MO7H3F6IDrL502vzWee75Hgg965Z4n6/2/MwHhZXoW037DrIsG2/uN/HeKZQzZJeaNudB+O5ecpq9/u1ngkEW5ILbXvoSDy3f/z3ifRyRxz1rQmFtj2YZufO6evc7xc4DtHCerTQtglpWdz16e/u99849tHRGlto20TDl/v+u8H9fqZ9D7VtBwttm2l4MHbWH+73H9r/pIFtb6FtAR76KgbX6Q7H79rX08K24axtH/96E6nknui+4rGUDh7Lz9p2yPZLOUZ1AJ7ymMudHovO2nbsuiD2G6EATPT4nNEe887a9prMF9lu1M+dzjaHR+xfnbXtwMzJbDCaAjDKNp9/22cCudsoEzvZeGBgYYurIXdlP0zO6dOsMba5TLDPPu98QznBPM8nqWVJco/LNOyk8PdJ6hWZb3Lq9PtJHp8ywrYIq+Xvn+Esw0YyvqQbnlye9ffP2Lv2d+hv+y3fcpMNb7Kwc8AIZmDWMwDYyWGB4zEiTh872YaNJHzd06x3NeXe7PHu9/u8biXJ8CH7jFPKaqTjaclmek4UT+cMB6Cx5TBfOp7Fk+x8P0cJhh8urDyffRtzXJcDcL11Fa/bp7iX6yAb/zOm6ZH5JgeMEEJI4HvPf1HLkpwvZyCpeFhcHDf8icx8j2w88CGDWY7naG/9izTDkzQ88SETH0smAHOcl/JQ9hgAapDMBq/7SDR8cWGhhiW3eHLUCKSnMY1tz/Q9676saFQIKAcMwyA921nmy/W220r07vpLly4lLS2N3r17U7t2bbp3786bb76Jr6/v+ScWEREpL3KyIDM59xvxnIyC/4Z3BL9aALiOxODcuYiczDScWWk4s9JxZaZhZKdjZKezp9k9HA1oQ3qWk5qxP9Np28vYnBnYXJl4uDLxMP5+zO300CdY5XMlWTku2qas5OGTT5814lOLY/ks+0pcBlxqjWGmY85Z2878eT0fO3NPjDpa/uQbz1NnbfvLjjje27oDgAhLHL0dgWRjI8ewkUPuy4kNJxZWxnvwQ2wcACGcIsrexj0+B2u+f5cn1SbmZO4Jjh9OPvS4Nn874+/22436JJ7+VjQTDx63jcKJFRdWnIYVF5bc/2PlsFHTnT0bG8OzJuLCgoEFJ9bcb6ANC4bFQiJ+eFgtWCy5lyre7HwOi+X0N9QAFhsuiwULFtI8vKnhcOQWHSwWbud1LBjYLLnT530nabVCNg4aVPPBYrFgAR42XsJ2+nTYesafWRYLZGOnue3vby+fdT17eq3/PpnLm8Rp9aC1zR/L6SHvOJ/AQdY/+hgYgAWnxUZ7j0D38OnOR5hlZOS7Btji7kVgpbO9uvv9/7Lv5wcjLXe+lr/nayG3x0E3zxruefyYfS+/ulJOr5fB6f4AWHFiAbp71sJyeqV/z7yVv5xX/z3eMHDvPcNFZ99wnJbcU5G9GX35b1bz3L1suPL2NpbT/28TXptMqzeGAcnpnfkx03G6rfN0+9wjxGY4aR5SmzBb7re21lONiElv725rO6OdDSdNagVTzSN3/WqlVifhVFDuEXl6vO2MaZqFBWJz5BYj6qd6Qe75IZ6WHDzJcW+jnrZNTLNPY5L9YbBY8M/x4IzRBdSq5kltqzdQhwmuSUzLehxPsk7POxtP/v6MCA/wIs2SWwiolZ2N1Zm/kOewOKlJMmkWL2oH/l1AqJmVCf+oeeWeXKeTZvXL19Yj00Le4Wg/PT93VnsGtX1z23oZGZAJAZa0QtfL39NKbb/ctmEuBzWzChbH8k6yQ3yg9ulv5evk5GDPyb/cM4VXs+O0egO1ecz1JB9nPVogZ252F8GBeT9r3vhnZoMBPpZMfMjM17aa3eJerzBXMmRBoOVUgfmF+Rfs4VCRWQzDMM7fTIojOTmZgIAAkpKS8Pf3zzcuIyODvXv30rBhQ7y8cg+mtKwcWk46e2WztGx7JgofR9FrQSNGjCAxMfGs1/jfdtttBAcH8+abbwLQvn17xo0bx4gRI9xtLBYLc+bMOe+9Bi5WYdtZREQqOWdO7gl8RlLuv0ER4PDFMAyyDvyOc9cSctKScKUn4cpIgoxkLFkpWLNSWNn6OQ55NeVUlpOWBz6n78E3zrqYhzz+xc/ODqRnO7neWMpr9mlnbTs2636+d0UCcI11DVMcZ/8mfEL2PXzl7AlAF8sOnrN/QgYOMnCQadjd/88wHMxxXcZqVysAQjnBNba1ZOIgx+aJ0+qJYXXgstpxWe3EedTmpEcwHjYrPtZsgo0EsDnA5gE2B5bT/7faHNg8bHhYrdhtFmxWC3abFQ+rBQ/buYdZLRZsFgtWqwWbldz31jOGnX5vdQ/DPcySry1Fns56erlWC1jIO8HPXbaF0/9a9EhhKQU5mbmFQWc2ODNz3zvP6P5fLRS8c4sGuZe9nOMLP6tHbkUJcj/DjNNn7BlJkHY8f9uaTcF6+vKD5NjcNoH1wGqH7DRIOZI7zmKFWs3+ni7xAGSlQfX6YLFBahxknT7RtTkgqPHfbVPiwLtG/vnlsXvnXhKUJ+Gv3HX/J78Q8PTP/YyB3J5MJ0/3JHH4gV9wbqbTl1jk215Zp3LH5VuuT+40J/dDjYbgcbrLv8uZ2z75cP72AXVyLysKqP33sIzk3HVKP/n3MO/qude8GM7caSD3/cm9f6+X1QN8a0HqUajVtOC6ljPnOg/9J/UIkBKRmJjIN998w8qVK93Dbr/9dj7++ON8hQAREZFzOnUckg7m/tGWmYwrPYnMU0lknTpJTloS+5uOIMFak5TMbEJ2z6b5XzOw56TimZOKw8jIN6t77C/wW3YEaVlOhll+YJL9s7Mu9qvlG1jmyv0DfIgthb72vC6/eSfff5+IH0mzknT6G+u/LGF8mdOTDOxkWz3JsXritHrh8vDEZfUiu0Y7unrWwNthw8fSk7eMplgd3ljs3lgcPljt3lgd3tgcXrTysNPBw4bDw4rDowN7bbfh6WHF4WHF28NKgM16epyVSJvVPc7hYcVhux0Pm+4BLVImPDz/Phk9H6vt75P387GdcWrmV8vd86hQ/uG5L3cmB3gHFt42sN6535+pWuj555enRqNzj89j94LgFvmH5d1f5J8cvgXb5glunv+91QZe/rmvf/L8x/0S8trlrd/ZWCyFr9f5tkUFpEJAOeBtt7HtmShTlltSZs2aRUZGRr57AhiGgcvl4s8//6Rp0/JfQRMRkVJybCc5hzeSnhhHVvIxnCnHME4dx5p+AntGAl80fYN9rmAS07K5Kv5jbkqd6Z7UCniffgHcvS78jOt099Ddvq/A4tINByn4kJiaTrKR+43Tdms9vsjpSbrVl0ybH5kevuR4VCPHUQ2Xoxo1fJsxyKcGvp42fO138p7jLny8PPH1tOHt8MDHbsPLbsPbYeVJDxvejtz3Xh698Xbcj6eHDZtV3zyLiEjFoEJAOWCxWIrVRb88+vjjj3n44YcLfPv/f//3f3zyySe89NJL5gQTEZGSkZ2e24X09Ddb6X/9RsaeVWQmx5OTcgzSTuCRfgJHVgLe2YlMqvUO27JDSEzL5vb0/zLa8jVnu5/14rWbWW/kdmOtaXNwmUcNUgxvUvAhxfAhFW9OWXzItPnhXT2Utj4B+HvZSbf1Y6qlCzafADx8AnH4BOLpF4Cfjw/+Xh7828uOj6cNX4cHPp598HVM0Mm6iIgIKgRIMSUlJbFx48Z8w1JSUtiwYQMzZ86kefP8XXZuueUWnnnmGZ577jk8PE7fEGbv3gLzaNKkiW4qKCJS1vJuE3T6GurMvatJ+3M5WScP40o+gi01DkfmcbyzE/F0pfN/1aexOSOYE6lZ3Gd8yYMe35x11nsP7Gerkfs9/jZrGL/aWpFANVJtgWQ4qpPpqIHTqwb41KRzjZZcUa06gT52An06sMv7Cfy97VT38qCel51qXh54ne7FdvZ72IuIiEhRqRAgxbJs2TI6dOiQb9iIESNo2bJlgSIAwI033sjYsWP54YcfuP766wEYP358gXYrVqzgsssuK53QIiJV2cl95MRu5lT8XjISDuJMOoI1NQ7P9Dh8Mo/zZMj7bEqvSVxSBiNzvmDcOU7uj8Ud5pCR+71+jLUh81yXkm6vTpZnDZzeNcC3Jh5+tXD4B3NHzfqM8at2+uS+J9V9nsLfy45V38iLiIiYToUAKbLo6Giio6OLNU1oaChO5993StVDKkRESojLBaeOQuJB0o7tJSV+L1nH92FJPsi34ePZnh7I4cR0+h2fzl2urwgAAgqZzaEDe/jTlft0lY3Wxsxx9SDVM5gs72CcvqFYq4Vg9w/Bp3oIdwdU57FqXtTy8yTILwofR8k+hlZERETKhgoBIiIi5ZFhQFoCJPxFevyf7Kt+KX+d8uRAQhr1/5zO1bFTsZ9+vrTP6VeeXw9fzipXawBqW0PZ6NGIWGqRZA8hwzsYp08otoAw7NVrc1Ot+owJ9CfE35OQgD5U8/TQyb2IiEglp0KAiIiIyQzDIGH3OjJivsN5fA+OpL34px/Ex5UK5N4x/5msJ9zPjb/ZlsG19mychoV4qnPIqMVxWzApXmFk+9Wmc+glXBlcn9qB3tSufinhgf+mrY9D3fJFREQEUCFARESk9Dlz4OQ+0o9sJXF/DNlx23Gc3MNngaP5KbUh+xNOcb1rCa/YPywwaaxRg/2uUHy9PekYFEi9Gj7UrTaE73wHEhjagPAa/rQO9KrwT58RERGRsqO/GkREREpKTiaGy0lcuoU9R0+Rvv1H2m57lRoZB7GTjTe53+7nOZ4Yw05nTQC2WBvxne1qUv3q4wxsiCO4CdVrN6FOcE3aBPnwkad+ZYuIiEjJ0F8VIiIixWUYcHIfpw5uIvGvDTiPxOCdtIsamYeZZNzHzMzcp6B0sx7mC8dfAKQZnuw2wjlkq0uSXyOcQU1oX7cLfes0pEGQL7UDvXF4/J+ZayUiIiJVhAoBIiIi55KVRnZ2JruTbeyISyZl16/ctH0c3kYavoDvP5qHOQ/jYbVQL8iHWkGXMNvzTXxrtySsfhMa1/KnrY/djLUQERERcVMhQEREJE9mKlmHN3LizzVkHvgdnxNbCco8yDTXjbyWNQiAUDIZ5pVGpuHBLqMO++2NSPZvhiW0FdXrtaZ3g0bcU6saDg/r6ZleYd76iIiIiBRChQAREamaDINMp4sdR1LY9dceLls1ilqZ+3HgIuwfTWsbcVTz9KB5WDWah9Rjnt/XhDduQ5PwGrT20jf8IiIiUrGoECAiIlWCkXSYhJ0rSfrzVxxHfmebsy5jU0aQ5XRhxUWMZyw2i4sjRg22Wxpzwr8lRlh7qjfuRMfGEWyu4YPFkvf4vTamrouIiIjIxVAhQC6KxWJhzpw5DBgwoMTmGR0dzbhx40hMTCyxeYpI1ZORlUP8knfJ3ruKGgkbqZFzlCAg6PT4LNcJspwuqvvYaVMnkLl+7xDWoAVNIprQq7r3GSf9IiIiIpWLCgFyUY4cOUL16tXNjiEiVV1OJml717Bvz3bmcQVr/jpBzOEk5tk+prn1YG4Tw8oOox77vVuRHtqJak26s7xFW+q5v+m/xNx1EBERESkjKgTIBcvKyiI0NNTsGCJSFeVkkrJnNfGbf8LjwCrCUmLwIYuGhoOPMz8ki9zr9r/zjGJHoAtrva6Et7yUVg3Cae2wmRxeRERExFzW8zcRydWzZ0/Gjh3LuHHjqFmzJlFRUVgsFubOnQvAvn37sFgsfPHFF3Tv3h0vLy9at27N8uXL3fNYtmwZFouF+fPn07ZtW7y8vOjWrRtbtmw557K//fZbOnbsiJeXF40aNeLpp58mJyenNFdXRMqZtKwclu44ysqpY8l8rg7VPr+BiK3v0iBlPZ5kcczwZ7VHF25tF8Crg9vyy4ReTPj3awx44E2uHzCUzk3r4q0igIiIiEjFKQRMnjwZi8WS79W8eXP3+IyMDMaMGUNQUBB+fn4MGjSI+Pj4fPM4cOAA/fr1w8fHh+DgYCZMmFDgZHLZsmV07NgRT09PIiIiiI6OLovVy5V16uyv7IxitE0/f9sLNGPGDBwOB7/++itTp04ttM2ECRN4+OGH+eOPP4iMjKR///6cOHGiQJvXX3+ddevWUatWLfr37092dnah81uxYgXDhg3jwQcfZNu2bUybNo3o6Gief/75C14PESn/nAn7OLj4fXa/dyP3vD+fdk//yJ3R61hxMOf0iX8ASz0uY07tR1hy1TxyHtrJlf/+gcm39OKmznWpF+Sj6/xFREREClGhLg1o1aoVP/30k/u9h8ff8R966CHmz5/PV199RUBAAGPHjmXgwIH8+uuvADidTvr160doaCirVq3iyJEjDBs2DLvdzgsvvADA3r176devH/fddx8zZ85kyZIl3HXXXYSFhREVFVX6K/hC+NnHNekDt3319/tXIyA7rfC29S+DO+f//f6tNpCW/0ScyUkXFLFJkya88sor52wzduxYBg3Kfd72lClTWLhwIR9//DGPPvqou81TTz3F1VdfDeQWF+rUqcOcOXO4+eabC8zv6aef5rHHHmP48OEANGrUiGeffZZHH32Up5566oLWQ0TKoaw0TmxZzPFNCwiMXUFI9iHqnh7lk9WcbNdl1A70xtlgCEvr3k7rNp3p5e9lamQRERGRiqhCFQI8PDwKvSY9KSmJjz/+mFmzZnHllVcCMH36dFq0aMFvv/1Gt27d+PHHH9m2bRs//fQTISEhtG/fnmeffZaJEycyefJkHA4HU6dOpWHDhrz++usAtGjRgpUrV/Lmm2+WTSGgAujUqdN520RGRrr/7+HhQefOndm+fftZ29SoUYNmzZoVaJNn06ZN/Prrr/l6ADidTjIyMkhLS8PHx6e4qyEi5YTTZbDxYCJ//fYt12+fQBBZ7rv65xhWNluacKh6N3q27suD7TrTQN/yi4iIiFy0ClUI2LVrF+Hh4Xh5eREZGcmLL75IvXr1WL9+PdnZ2fTu3dvdtnnz5tSrV4/Vq1fTrVs3Vq9eTZs2bQgJCXG3iYqKYvTo0WzdupUOHTqwevXqfPPIazNu3Lhz5srMzCQzM9P9Pjk5+cJW8F+xZx9n+cd1rRN2n6PtP674GBdzYXkK4evrW2LzKqrU1FSefvppBg4cWGCcl5e+DRSpUFwuUvetI3btHNamBPFGXHsSTmVREweDPLM5RE22+lxCdoMrqdOpD20b1qWjrcJcxSYiIiJSIVSYQkDXrl2Jjo6mWbNmHDlyhKeffprLL7+cLVu2EBcXh8PhIDAwMN80ISEhxMXFARAXF5evCJA3Pm/cudokJyeTnp6Ot7d3odlefPFFnn766YtfSUcxTrJLq20J+O233+jRowcAOTk5rF+/nrFjxxZoU69ePQBOnjzJn3/+SYsWLQqdX8eOHdm5cycRERGlG1xESoczh5M7lnF0zWyCDy2muiuBpsAJZ0sSsltSzcuDbk2b81Pd+XTu0JkoP0+zE4uIiIhUahWmEHDNNde4/9+2bVu6du1K/fr1mT179llP0MvK448/zvjx493vk5OTqVu37jmmqNz+85//0KRJE1q0aMGbb77JyZMnGTlyZL42zzzzDEFBQYSEhPDEE09Qs2ZNBgwYUOj8Jk2axHXXXUe9evUYPHgwVquVTZs2sWXLFp577rkyWCMRuRCHEk6R9M1D1Dm8kOpGEtVPD08xvNlg70Bioz58cVk3OtWvjl3f+ouIiIiUmQpTCPinwMBAmjZtyu7du7n66qvJysoiMTExX6+A+Ph49z0FQkNDWbt2bb555D1V4Mw2/3zSQHx8PP7+/ucsNnh6euLpqW+w8rz00ku89NJLbNy4kYiICL777jtq1qxZoM2DDz7Irl27aN++PfPmzcPhcBQ6v6ioKL7//nueeeYZXn75Zex2O82bN+euu+4qi9URkaLKySJ220rmnKjHwi1xxBxO4mP7n7SyJXHS8ON37+5kN+1Py0uv44qQGmanFREREamyKmwhIDU1lT179nDHHXfQqVMn7HY7S5Yscd+tfufOnRw4cMB9U7rIyEief/55jh49SnBwMACLFy/G39+fli1butv88MMP+ZazePHifDe2q8qWLVtWYJhhGAWGtWjRgjVr1pxzXpdddhlbtmwpdNyIESMYMWJEvmFRUVG6YaNIeeRycXLbzxxd9Rm1jywm3DjFrIy3OUwtrBZYFnwHOfXvoc2l13F1kL/ZaUVERESEClQIeOSRR+jfvz/169cnNjaWp556CpvNxi233EJAQACjRo1i/Pjx1KhRA39/f+6//34iIyPp1q0bAH369KFly5bccccdvPLKK8TFxfHkk08yZswY97f59913H++99x6PPvooI0eO5Oeff2b27NnMnz//XNFERKqc1AObOLQsmlr7vyPIedzd7f+YEcB1dTOo37kNfVqFUFPX+4uIiIiUOxWmEHDo0CFuueUWTpw4Qa1atbjsssv47bffqFWrFgBvvvkmVquVQYMGkZmZSVRUFO+//757epvNxvfff8/o0aOJjIzE19eX4cOH88wzz7jbNGzYkPnz5/PQQw/x9ttvU6dOHT766CN9Ey0iAmQ7Xfy84yh/rZjN6Lh/0/z08GTDh9+8e+BqPZhOl/fj8QA90lNERESkPLMYhfXtlouSnJxMQEAASUlJ+Pvn7wqbkZHB3r17adiwoR59V4q0nUVKSE4WsevmsHJPIq/sa8Tx1Cx8yGCV5/1ssbchpelAWl1xE/V0zb+IiIiIqc51HvpPFaZHgIiIlJ3Uw9s48NMHhO+bQ7iRSFtXXY5nvURNPy8GdmzEkdYbubRuLSwWi9lRRURERKSYVAgwiTpilC5tX5HiM7JOsW/FLFy/f0rj9M20PD38qBHIXzUu46Or2nFFy9p61J+IiIhIBadCQBmz2+0ApKWlnfORhHJx0tLSgL+3t4icXVpWDt9ujCVs8Rh6Zv8CgNOwsMbehZQWt9Dxqpu5NtDP5JQiIiIiUlJUCChjNpuNwMBAjh49CoCPj4+61pYgwzBIS0vj6NGjBAYGYrPZzI4kUj65XMSun8eX+/34JCaHlMwcLrNeSmP7DraE3EDtnqOIbNFcn08iIiIilZAKASYIDQ0FcBcDpOQFBga6t7OI/M2ZdpLdi6YSsGUG4c4jeOdcR0rOrTQI8qFn15uo1mkc1/jqBpsiIiIilZkKASawWCyEhYURHBxMdna22XEqHbvdrp4AIv+Qfmwv+75/jfr7/0czMoDcx/6F1Qri02su4bKImlit+vZfREREpCpQIcBENptNJ6wiUqqOJmdw5L/30OroPFrgAmA3ddnV8DbaXnM3NwTXNDmhiIiIiJQ1FQJERCobw2BnXAofrNjLd5sO8y9LBu08XKyztiOx/b1073MzEV66kaaIiIhIVaVCgIhIZeFysn/FLGy/vs6/Uoax3mgGwOo6t9O0/Wi6de+JTd3/RURERKo8FQJERCo6Zw57lkbj/dub1M85BMAIj0UEN7uCe3o0okO96iYHFBEREZHyRIUAEZEKysjJZNfij/D//V0aO48AkGj48lvwzbS6/hH6161jckIRERERKY9UCBARqYB+3X2cgC8H0Do7BoAEoxrrwm6l5Q0P0zcsxOR0IiIiIlKeqRAgIlJRuFysP3CS137cxeq/TnC7rTMPehzgj7p30HbAQ0TV1BMAREREROT8VAgQESnvDIP9a+bCz8/y39Q+rHZdjsNmxbPLCIweT9Gnhu4BICIiIiJFp0KAiEg5Frv5Z9IWTCIiPfcSgLs9fsDR5hbu792EOtV9TE4nIiIiIhWRCgEiIuVQ0oGtxH09gWZJvwKQadhZWeNGGt/4b16uV8/kdCIiIiJSkakQICJSjmTmONnw5Qt03vUmzXCSY1hZWa0vtW+YzFVNmpkdT0REREQqARUCRETKAcMwmB9zhJcX7iD8pDdfejr5zeMS7H2fo2fnrmbHExEREZFKRIUAEREzGQb7V/+PBas38dKxSAAyq7VnUeT/6N2zNzarxeSAIiIiIlLZqBAgImKS5MM7OfLlgzRLXs0ww5MvHC25sUcX7u7REB+HPp5FREREpHToL00RkTLmykxj61fP0HT3RzQjmyzDxq9Bg/jylj6E1KppdjwRERERqeRUCBARKUN//fo13kv+RRtXHAAbPNphufZ1ru7YxeRkIiIiIlJVqBAgIlIGUjKymTpvJQ9suQdPSw7xRg22tH2MHjfchd3DZnY8EREREalCVAgQESllP22L59/fbuFIUgZ2j+tpG+ygzW3Pc1WQLgMQERERkbKnQoCISCk5sX8LJ74Yw1uJgzliNKJ+kA9dbnyVSyNUABARERER86gQICJSwoycLLZ89SxNd04hiGwm2TP4OXIGD17VBG+HLgMQEREREXOpECAiUoKO7vqd9Nl30yb7LwDW2zsRMOhdHmve3ORkIiIiIiK5VAgQESkBhjObLV89R/Pt7xJscXLSqMbGVhO5fOD/4aGbAYqIiIhIOaJCgIjIRTqWksm3n77BXcfeAgv85ogk9Lap9KrfwOxoIiIiIiIFqBAgInIRfog5whNzYkhMa019R2esLftzxeD71QtARERERMotFQJERC5A6vGDxPx3Ig/HDSIdL1qEBVLn5jm0CPM3O5qIiIiIyDmpECAiUkx/rZpD9R8fJJIkHvXI4cTlz/LAVU1weFjNjiYiIiIicl4qBIiIFJErK4Mtn46n7aGZAOyyNKDL4Edo3b6ZyclERERERIpOhQARkSI4cWAbyZ/dQdvs3QAsDbiRjqPeJcC/msnJRERERESKR4UAEZHziFn2PxotG0MQGSQafmzu/AI9rxuGxWIxO5qIiIiISLFV2AtaX3rpJSwWC+PGjXMPy8jIYMyYMQQFBeHn58egQYOIj4/PN92BAwfo168fPj4+BAcHM2HCBHJycvK1WbZsGR07dsTT05OIiAiio6PLYI1EpLxxuQze+ulP7l2URoZhZ7OtNSeH/0yP/sNVBBARERGRCqtCFgLWrVvHtGnTaNu2bb7hDz30EPPmzeOrr75i+fLlxMbGMnDgQPd4p9NJv379yMrKYtWqVcyYMYPo6GgmTZrkbrN371769etHr1692LhxI+PGjeOuu+5i0aJFZbZ+ImK+kwknuDN6HW/9tItYI4joFh/Q9NGlNGyk+wGIiIiISMVmMQzDMDtEcaSmptKxY0fef/99nnvuOdq3b89bb71FUlIStWrVYtasWQwePBiAHTt20KJFC1avXk23bt1YsGAB1113HbGxsYSEhAAwdepUJk6cyLFjx3A4HEycOJH58+ezZcsW9zKHDh1KYmIiCxcuLFLG5ORkAgICSEpKwt9fjxITqWj2rP6OGovGMDFrFMutXXnhxjYM6lTH7FgiIiIiImdVnPPQCtcjYMyYMfTr14/evXvnG75+/Xqys7PzDW/evDn16tVj9erVAKxevZo2bdq4iwAAUVFRJCcns3XrVnebf847KirKPY/CZGZmkpycnO8lIhWP4XKyaeYTNFw4jOokc6/XT8wZ3V1FABERERGpVCrUzQK/+OILNmzYwLp16wqMi4uLw+FwEBgYmG94SEgIcXFx7jZnFgHyxueNO1eb5ORk0tPT8fb2LrDsF198kaeffvqC10tEzJd56iR7pt5Gu5RfwQK/VLuWdvd8QEA1PRVARERERCqXCtMj4ODBgzz44IPMnDkTLy8vs+Pk8/jjj5OUlOR+HTx40OxIIlIMJw7uJP6NK2iZ8iuZhp2lzSZx+fhZKgKIiIiISKVUYQoB69ev5+jRo3Ts2BEPDw88PDxYvnw577zzDh4eHoSEhJCVlUViYmK+6eLj4wkNDQUgNDS0wFME8t6fr42/v3+hvQEAPD098ff3z/cSkYph5+5d2D6+inrO/RylOluiPqfXLQ/rqQAiIiIiUmlVmELAVVddRUxMDBs3bnS/OnfuzG233eb+v91uZ8mSJe5pdu7cyYEDB4iMjAQgMjKSmJgYjh496m6zePFi/P39admypbvNmfPIa5M3DxGpPH6IOcKAGXuYk9OdHdYI0oYvplP3q82OJSIiIiJSqirMPQKqVatG69at8w3z9fUlKCjIPXzUqFGMHz+eGjVq4O/vz/33309kZCTdunUDoE+fPrRs2ZI77riDV155hbi4OJ588knGjBmDp6cnAPfddx/vvfcejz76KCNHjuTnn39m9uzZzJ8/v2xXWERKjSs7iyk/beHV5UcAWBYxjoE3tSYgQL15RERERKTyqzCFgKJ48803sVqtDBo0iMzMTKKionj//ffd4202G99//z2jR48mMjISX19fhg8fzjPPPONu07BhQ+bPn89DDz3E22+/TZ06dfjoo4+IiooyY5VEpIRlnkrkr/8MpmPKKTx4jOGXNeHxa5rjYaswHaRERERERC6KxTAMw+wQlU1xnt8oImUnOf4AJz4cQMOcPaQZnqy8/FP69O5rdiwRERERkYtWnPPQStUjQETkbOJ2bcA66yYaGsc5YQRw8Jpo+nS70uxYIiIiIiJlToUAEan09qydT8gPd+FHGvuoTc6ts2nfrPX5JxQRERERqYRUCBCRSi1m8QyarXwIh8XJFltLat7zNaEh4WbHEhERERExje6OJSKV1v/WH2LCsgwy8OQ37yuo/9CPKgKIiIiISJWnHgEiUil9snIvz3y/DajDf5p/wCO3XIPdQx95IiIiIiL6q1hEKhXD5WTDh2NYsK8J0JxRlzVk4rUtsFotZkcTERERESkXVAgQkUrDlZ1FzPu30unkYj50+PJV93nc1acFFouKACIiIiIieVQIEJFKITsjlT/fHUS7U7+RbdiIaT+Ju6M6mR1LRERERKTcUSFARCq8zLQk9r3Tn1YZm0g3HGyMfJvL+95qdiwRERERkXJJhQARqdAyUhPZ/+51NMuMIcXwZlfvT4i8/FqzY4mIiIiIlFt6fKCIVFgZ2U5+/OBxmmXGkGz4sKfvTDqqCCAiIiIick7qESAiFVJ6lpO7P/2dtUejsDj2U++6R2h/yZVmxxIRERERKfdUCBCRCictNYlRM7exem8CPg4vgkd8RrtGQWbHEhERERGpEHRpgIhUKGlJxzny9lVcffAtfB1WPh15CV1VBBARERERKTL1CBCRCiMjNZHY//QjInsX1W1xdB4ymbYNapgdS0RERESkQlGPABGpELLSU9n3bn8isnaQaPhxZMBXtG3ZwuxYIiIiIiIVjgoBIlLu5WSms+vdATTP3EyK4c2BfjNp1SHS7FgiIiIiIhWSCgEiUq65srPY/u5gWqWtI83wZHefaNpe0tPsWCIiIiIiFZYKASJSbhmGwUeff07LlF/JNOxsu2IaHS7ta3YsEREREZEKTTcLFJFy66UFO5i2rSabbWO5vUdLul15o9mRREREREQqPBUCRKRc+mTpdqb98hcAPQbcS7cudU1OJCIiIiJSOejSABEpdzZ+8yqXLx1EGCd47Jrm3KwigIiIiIhIiVGPABEpV7YunkHbTc9jtRo8HbGLq3vcYXYkEREREZFKRT0CRKTc2LVmPk1WjsdqMVgZeD2973wai8VidiwRERERkUpFhQARKRcObvuNsAWjcFhyWOd9GZf83ydYbfqIEhEREREpaforW0RMd+LwHrxnD8WPdGI82tBy7Jc4HHazY4mIiIiIVEoqBIiIqdKznOydcS81OckeS31qj56Dr6+f2bFERERERCotFQJExDQul8H42RsZnTyC5XTCPuwragTVMjuWiIiIiEilpqcGiIhpXlm0kwVb4rDbauA17CvqNQwyO5KIiIiISKWnQoCImGL97BeJ3ZgCXMorg9vStZGKACIiIiIiZUGFABEpc9t+/pwOW1+mk8Mgsm0nbuxQx+xIIiIiIiJVhu4RICJl6uD2NTT45UGsFoNfA69n6IAbzY4kIiIiIlKlqBAgImUm+UQc9tm340MmGx0d6fx/H2Gx6mNIRERERKQs6S9wESkTzuwsDn9wM6HGUQ5awqh7zxd4OjzNjiUiIiIiUuWoECAiZWLjx2NokbmJU4YXGYM+I6hmiNmRRERERESqJBUCRKTUzdt4mLWH0nEZFrZ0e40mrbuYHUlEREREpMqqMIWAKVOm0LZtW/z9/fH39ycyMpIFCxa4x2dkZDBmzBiCgoLw8/Nj0KBBxMfH55vHgQMH6NevHz4+PgQHBzNhwgRycnLytVm2bBkdO3bE09OTiIgIoqOjy2L1RCqtrbFJTPh6My/n3MJHbWfR9Zo7zI4kIiIiIlKlVZhCQJ06dXjppZdYv349v//+O1deeSU33HADW7duBeChhx5i3rx5fPXVVyxfvpzY2FgGDhzont7pdNKvXz+ysrJYtWoVM2bMIDo6mkmTJrnb7N27l379+tGrVy82btzIuHHjuOuuu1i0aFGZr69IZZBw9DBjZvxGRraLK5rWYtSN15gdSURERESkyrMYhmGYHeJC1ahRg1dffZXBgwdTq1YtZs2axeDBgwHYsWMHLVq0YPXq1XTr1o0FCxZw3XXXERsbS0hI7rXJU6dOZeLEiRw7dgyHw8HEiROZP38+W7ZscS9j6NChJCYmsnDhwiLnSk5OJiAggKSkJPz9/Ut2pUUqCGd2Frtf7UlqRhbP+z7O9PuvJ8DHbnYsEREREZFKqTjnoRWmR8CZnE4nX3zxBadOnSIyMpL169eTnZ1N79693W2aN29OvXr1WL16NQCrV6+mTZs27iIAQFRUFMnJye5eBatXr843j7w2efM4m8zMTJKTk/O9RKq6P6aPo1nWVppaDvHagCYqAoiIiIiIlBMVqhAQExODn58fnp6e3HfffcyZM4eWLVsSFxeHw+EgMDAwX/uQkBDi4uIAiIuLy1cEyBufN+5cbZKTk0lPTz9rrhdffJGAgAD3q27duhe7qiIV2pafPqNz7EwAtl3yMo2atTU5kYiIiIiI5KlQhYBmzZqxceNG1qxZw+jRoxk+fDjbtm0zOxaPP/44SUlJ7tfBgwfNjiRimri9W6m/cgIAK2rdQtd+w01OJCIiIiIiZ/IwO0BxOBwOIiIiAOjUqRPr1q3j7bffZsiQIWRlZZGYmJivV0B8fDyhoaEAhIaGsnbt2nzzy3uqwJlt/vmkgfj4ePz9/fH29j5rLk9PTzw9PS96/UQquqz0U6T/9zZCSWerRysuuestsyOJiIiIiMg/VKgeAf/kcrnIzMykU6dO2O12lixZ4h63c+dODhw4QGRkJACRkZHExMRw9OhRd5vFixfj7+9Py5Yt3W3OnEdem7x5iMi5bfx4LA2dezlBAIHD/ounp5fZkURERERE5B8qTI+Axx9/nGuuuYZ69eqRkpLCrFmzWLZsGYsWLSIgIIBRo0Yxfvx4atSogb+/P/fffz+RkZF069YNgD59+tCyZUvuuOMOXnnlFeLi4njyyScZM2aM+9v8++67j/fee49HH32UkSNH8vPPPzN79mzmz59v5qqLVAjzNsXyxuFLec++nsyrnqVjvUZmRxIRERERkUJUmELA0aNHGTZsGEeOHCEgIIC2bduyaNEirr76agDefPNNrFYrgwYNIjMzk6ioKN5//3339Dabje+//57Ro0cTGRmJr68vw4cP55lnnnG3adiwIfPnz+ehhx7i7bffpk6dOnz00UdERUWV+fqKVCQHTqTxr29iSDHC+KH7F0y4oqXZkURERERE5CwshmEYZoeobIrz/EaRii47M50npsxidlwonetX54t7uuFhq9BXHYmIiIiIVDjFOQ+tMD0CRKR82jT9QV48OZtaXndwy9CXVQQQERERESnnVAgQkQu2bdlsOsd9CRbo2f1S6lT3MTuSiIiIiIich766E5ELkhC3n7BlDwOwsubNdOkz1OREIiIiIiJSFCoEiEixGc4c4qKHU51kdlsb0mnk22ZHEhERERGRIlIhQESKbf3nz9Ay4w/SDE+sN0/H20eXBIiIiIiIVBQqBIhIsezbuYm2u94DYGPrx2nUvIPJiUREREREpDh0s0ARKbJsp4v7FyXTKudO+gfso/ugB82OJCIiIiIixaQeASJSZO/+vJuY2GQWekYRcd9MLFZ9hIiIiIiIVDTqESAiRbJj81pmLD0AePPcgNaE+HuZHUlERERERC6ACgEicl7pKYkEzLmN+XYXnzV8hevahpsdSURERERELpD69YrIeW2LHkuYcRSrxcLoG3qaHUdERERERC6CCgEick7bls2m04l5uAwLR696i8DqQWZHEhERERGRi6BCgIicVWrSCYKXPQrAquCbaX/5dSYnEhERERGRi6VCgIic1c4ZD1CTkxywhNNxxOtmxxERERERkRKgQoCIFGr7yrl0Svgel2Eh6eo38fGtZnYkEREREREpAXpqgIgUkJ7l5F+rDEY5u1IjuA7du/c1O5KIiIiIiJSQC+oRsGLFCm6//XYiIyM5fPgwAJ999hkrV64s0XAiYo43f/qTPxIcPOc9kdaj/mN2HBERERERKUHFLgR8/fXXREVF4e3tzR9//EFmZiYASUlJvPDCCyUeUETK1pbd+/loxV8APH9ja/x9vE1OJCIiIiIiJanYhYDnnnuOqVOn8uGHH2K3293DL730UjZs2FCi4USkbGWlnyJwVl/e8XibW9v4clWLELMjiYiIiIhICSv2PQJ27txJjx49CgwPCAggMTGxJDKJiEk2/fcxurhicdgyuLRvK7PjiIiIiIhIKSh2j4DQ0FB2795dYPjKlStp1KhRiYQSkbJ3YPta2h+aCcD+bs9QPaiWyYlERERERKQ0FLsQcPfdd/Pggw+yZs0aLBYLsbGxzJw5k0ceeYTRo0eXRkYRKWWGy0n6Nw9itzhZ73MZnaNuNzuSiIiIiIiUkmJfGvDYY4/hcrm46qqrSEtLo0ePHnh6evLII49w//33l0ZGESllG+a+Q6fsbZwyvAgb8jYWi8XsSCIiIiIiUkoshmEYFzJhVlYWu3fvJjU1lZYtW+Ln51fS2Sqs5ORkAgICSEpKwt/f3+w4IueUePQwlvcvIYBUfm38MJfeMcnsSCIiIiIiUkzFOQ8tdo+APA6Hg5YtW17o5CJSTny2cCUDDU+O2oK5ZOhjZscREREREZFSVqRCwMCBA4s8w2+++eaCw4hI2Vq3L4HXt/kxhVf54rbG2O0OsyOJiIiIiEgpK1IhICAgoLRziEgZy8px8cScGACu79KEtm3ampxIRERERETKQpEKAdOnTy/tHCJSxtZ9/iwdjydzwqcPE/s2NzuOiIiIiIiUkQu+R4CIVFzx+3fQefe7XGrP5rounanuq0sCRERERESqimIXAjp06FDoo8UsFgteXl5EREQwYsQIevXqVSIBRaTkHfnqEUIs2cQ42nFp1BCz44iIiIiISBmyFneCvn378tdff+Hr60uvXr3o1asXfn5+7Nmzhy5dunDkyBF69+7Nt99+Wxp5ReQibVv5Le1TV5BjWPG+/jUs1mJ/DIiIiIiISAVW7B4Bx48f5+GHH+bf//53vuHPPfcc+/fv58cff+Spp57i2Wef5YYbbiixoCJy8XKyMvH9+QkA1tYaSPfWl5icSEREREREyprFMAyjOBMEBASwfv16IiIi8g3fvXs3nTp1IikpiR07dtClSxdSUlJKNGxFkZycTEBAAElJSfj7+5sdR8Rt3efP0WXnqyRQDev9fxAYVMvsSCIiIiIiUgKKcx5a7D7BXl5erFq1qsDwVatW4eXlBYDL5XL/X0TKh4STCTTb+T4AO1uNVxFARERERKSKKvalAffffz/33Xcf69evp0uXLgCsW7eOjz76iH/9618ALFq0iPbt25doUBG5OK8vO8yWzIncV20lVw+43+w4IiIiIiJikmL3CHjyySf58MMPWbt2LQ888AAPPPAAa9eu5cMPP+SJJ3KvPb7vvvuYN29eiQZ98cUX6dKlC9WqVSM4OJgBAwawc+fOfG0yMjIYM2YMQUFB+Pn5MWjQIOLj4/O1OXDgAP369cPHx4fg4GAmTJhATk5OvjbLli2jY8eOeHp6EhERQXR0dImui0hZ23I4iVlrD7DJiKD60Kl42O1mRxIREREREZNc0O3Cb7vtNlavXk1CQgIJCQmsXr2aW2+91T3e29u7xC8NWL58OWPGjOG3335j8eLFZGdn06dPH06dOuVu89BDDzFv3jy++uorli9fTmxsLAMHDnSPdzqd9OvXj6ysLFatWsWMGTOIjo5m0qRJ7jZ79+6lX79+9OrVi40bNzJu3DjuuusuFi1aVKLrI1JWDJeL9+YuxzCgf7twujUKMjuSiIiIiIiYqNg3C8yTlZXF0aNHcblc+YbXq1evRIKdz7FjxwgODmb58uX06NGDpKQkatWqxaxZsxg8eDAAO3bsoEWLFqxevZpu3bqxYMECrrvuOmJjYwkJCQFg6tSpTJw4kWPHjuFwOJg4cSLz589ny5Yt7mUNHTqUxMREFi5cWKRsulmglCcbF3xEi98eY5oxgMHj3yU80NvsSCIiIiIiUsJK9WaBu3bt4vLLL8fb25v69evTsGFDGjZsSIMGDWjYsOEFhy6upKQkAGrUqAHA+vXryc7Opnfv3u42zZs3p169eqxevRqA1atX06ZNG3cRACAqKork5GS2bt3qbnPmPPLa5M2jMJmZmSQnJ+d7iZQHWRlphK59CU9LNp3q11ARQEREREREin+zwBEjRuDh4cH3339PWFgYFoulNHKdk8vlYty4cVx66aW0bt0agLi4OBwOB4GBgfnahoSEEBcX525zZhEgb3zeuHO1SU5OJj09HW/vgidSL774Ik8//XSJrJtISdr4v5e5xDhGPDVoN+RJs+OIiIiIiEg5UOxCwMaNG1m/fj3NmzcvjTxFMmbMGLZs2cLKlStNy3Cmxx9/nPHjx7vfJycnU7duXRMTiUDy8Tia7/4AgL1tx9PNT5epiIiIiIjIBVwa0LJlS44fP14aWYpk7NixfP/99yxdupQ6deq4h4eGhpKVlUViYmK+9vHx8YSGhrrb/PMpAnnvz9fG39+/0N4AAJ6envj7++d7iZhtx+wn8SeN3dZGdLnh/8yOIyIiIiIi5USxCwEvv/wyjz76KMuWLePEiRNldm28YRiMHTuWOXPm8PPPPxe4H0GnTp2w2+0sWbLEPWznzp0cOHCAyMhIACIjI4mJieHo0aPuNosXL8bf35+WLVu625w5j7w2efMQqQgO795Mh/hvADjV82lsNpvJiUREREREpLwo9lMDrNbc2sE/7w1gGAYWiwWn01ly6c7wf//3f8yaNYtvv/2WZs2auYcHBAS4v6kfPXo0P/zwA9HR0fj7+3P//fcDsGrVKiD38YHt27cnPDycV155hbi4OO644w7uuusuXnjhBSD38YGtW7dmzJgxjBw5kp9//pkHHniA+fPnExUVVaSsemqAmO3jqa9x65GX2eHdgQ6P/Wh2HBERERERKWXFOQ8tdiFg+fLlZx0XExPD2LFjizO7IjvbTQmnT5/OiBEjAMjIyODhhx/m888/JzMzk6ioKN5//313t3+A/fv3M3r0aJYtW4avry/Dhw/npZdewsPj79slLFu2jIceeoht27ZRp04d/v3vf7uXURQqBIiZ1u9PYNCU1YRZEvjvnR1p3LSV2ZFERERERKSUlWoh4J9SUlL4/PPP+eijj1i/fn2p9QioSFQIELMYhsGgKavYcCCRoV3q8tKgtmZHEhERERGRMlCc89Bi3yMgzy+//MLw4cMJCwvjtdde48orr+S333670NmJSAlYv/w7rAd/w9tuY/zVTc2OIyIiIiIi5VCxHh8YFxdHdHQ0H3/8McnJydx8881kZmYyd+5c9832RMQczpxsQn55nP95HmZ+46cI9u9rdiQRERERESmHitwjoH///jRr1ozNmzfz1ltvERsby7vvvlua2USkGDbOe5+6rsOcpBqXXTfc7DgiIiIiIlJOFblHwIIFC3jggQcYPXo0TZo0Kc1MIlJMWRlp1Nn8NgDbG99F9+pBJicSEREREZHyqsg9AlauXElKSgqdOnWia9euvPfeexw/frw0s4lIEW365jVCjBPEE0SHgY+YHUdERERERMqxIhcCunXrxocffsiRI0e49957+eKLLwgPD8flcrF48WJSUlJKM6eInMWp5ASa/PkBAHtbjcXb18/kRCIiIiIiUp4V+6kBvr6+jBw5kpUrVxITE8PDDz/MSy+9RHBwMNdff31pZBSRc9j6vxcIJIUDlnA6DRhrdhwRERERESnnLvjxgQDNmjXjlVde4dChQ3z++ecllUlEiujkqSy+2u/DIaMm8Z0fwW53mB1JRERERETKOYthGIbZISqb5ORkAgICSEpKwt/f3+w4Uom9+MN2pv3yF21DvZl7/xVYbTazI4mIiIiIiAmKcx56UT0CRMQ8x1IymbF6HwDj+rZSEUBERERERIqkyI8PFJHyJeaLp7jBlcPuOv3p1SzY7DgiIiIiIlJBqBAgUgEdj93LpYc+4kp7NhvbXoHFYjE7koiIiIiIVBC6NECkAvpr7vN4WrLZam9Fu8v6mR1HREREREQqEBUCRCqY44f30i5+LgA5lz+GxaofYxERERERKTqdQYhUMH/NffZ0b4DWtL3sOrPjiIiIiIhIBaNCgEgFcvzwX7Q/+i0AzivUG0BERERERIpPZxEiFchfc57FYclhi70NbS5VbwARERERESk+FQJEKoj45Axej2vHCmdrXFc8ricFiIiIiIjIBdHjA0UqiCnL9rAmpwmuBq8y+9JIs+OIiIiIiEgFpR4BIhXA8dRMvlh3AIAHr2qq3gAiIiIiInLBVAgQqQB2fPEE44z/ckVtF5dGBJkdR0REREREKjBdGiBSziUnHKXDwc+4zCODS1r0V28AERERERG5KOoRIFLObf/2NXwtGeyxNqB9r5vNjiMiIiIiIhWcCgEi5Vh6ahLN9s8EIKHj/Vht+pEVEREREZGLo7MKkXIs5tu3CCSVg5ZwOkQNNzuOiIiIiIhUAioEiJRTWRlpNNw1HYDDre7Dw243OZGIiIiIiFQGKgSIlFObv3+fWpwkniA69L/H7DgiIiIiIlJJ6KkBIuWQ02Xw8p56XJsTRZNWnbnM09vsSCIiIiIiUkmoECBSDv0Qc4R1J33Z7XMXKwdeaXYcERERERGpRHRpgEg5YxgG037ZA8Dw7g3w9VS9TkRERERESo4KASLlzLZf53H/0cl0s+9mWGQDs+OIiIiIiEglo68aRcoZ18q3ibL9jn/NhtTwdZgdR0REREREKhn1CBApR/ZvW0ObjN9xGhbqXvuw2XFERERERKQSUiFApBw59uPrAPxR7QrqNGphchoREREREamMVAgQKSeOH/6Ldid/AsCv10MmpxERERERkcqqQhUCfvnlF/r37094eDgWi4W5c+fmG28YBpMmTSIsLAxvb2969+7Nrl278rVJSEjgtttuw9/fn8DAQEaNGkVqamq+Nps3b+byyy/Hy8uLunXr8sorr5T2qomwZ/4b2C1Ottrb0LxTT7PjiIiIiIhIJVWhCgGnTp2iXbt2/Oc//yl0/CuvvMI777zD1KlTWbNmDb6+vkRFRZGRkeFuc9ttt7F161YWL17M999/zy+//MI999zjHp+cnEyfPn2oX78+69ev59VXX2Xy5Ml88MEHpb5+UnWdSk6gZez/AMi8ZIzJaUREREREpDKzGIZhmB3iQlgsFubMmcOAAQOA3N4A4eHhPPzwwzzyyCMAJCUlERISQnR0NEOHDmX79u20bNmSdevW0blzZwAWLlzItddey6FDhwgPD2fKlCk88cQTxMXF4XDk3rH9scceY+7cuezYsaNI2ZKTkwkICCApKQl/f/+SX3mpdD77ZTu7Fk3hWq8tdPnXYmw2m9mRRERERESkAinOeWiF6hFwLnv37iUuLo7evXu7hwUEBNC1a1dWr14NwOrVqwkMDHQXAQB69+6N1WplzZo17jY9evRwFwEAoqKi2LlzJydPniyjtZGqJMfpYtrqI3zqjGL31dNVBBARERERkVLlYXaAkhIXFwdASEhIvuEhISHucXFxcQQHB+cb7+HhQY0aNfK1adiwYYF55I2rXr16gWVnZmaSmZnpfp+cnHyRayNVyaKt8Rw6mU4NXweDO9UxO46IiIiIiFRylaZHgJlefPFFAgIC3K+6deuaHUkqEK8FD3KTbRnDu4TgZVdvABERERERKV2VphAQGhoKQHx8fL7h8fHx7nGhoaEcPXo03/icnBwSEhLytSlsHmcu458ef/xxkpKS3K+DBw9e/ApJlbBn4y9clb6I5z0+5rZ2gWbHERERERGRKqDSFAIaNmxIaGgoS5YscQ9LTk5mzZo1REZGAhAZGUliYiLr1693t/n5559xuVx07drV3eaXX34hOzvb3Wbx4sU0a9as0MsCADw9PfH398/3EimKk0vfBWBTwJXUDKtnchoREREREakKKlQhIDU1lY0bN7Jx40Yg9waBGzdu5MCBA1gsFsaNG8dzzz3Hd999R0xMDMOGDSM8PNz9ZIEWLVrQt29f7r77btauXcuvv/7K2LFjGTp0KOHh4QDceuutOBwORo0axdatW/nyyy95++23GT9+vElrLZXViSMHaJuYW7jy7/mAyWlERERERKSqqFA3C/z999/p1auX+33eyfnw4cOJjo7m0Ucf5dSpU9xzzz0kJiZy2WWXsXDhQry8vNzTzJw5k7Fjx3LVVVdhtVoZNGgQ77zzjnt8QEAAP/74I2PGjKFTp07UrFmTSZMmcc8995TdikqVsHvBO3S1ONnu0YIWHXuYHUdERERERKoIi2EYhtkhKpviPL9RqqasjHRSXmpGEEms6/w6Xa67y+xIIiIiIiJSgRXnPLRCXRogUlnE/BhNEEnEU4N2V99hdhwREREREalCKtSlASKVxVd/2TjlbINH4x509/Q0O46IiIiIiFQhKgSIlLE/Dpzki7jafGN7glWDep1/AhERERERkRKkSwNEytj0X/cB0L9dODWreZ27sYiIiIiISAlTIUCkDB0/vJcm294hmJPceWkDs+OIiIiIiEgVpEsDRMrQnkX/4X7bN/Ty2k3r2rebHUdERERERKog9QgQKSM5WZk0OvA1AOltVQQQERERERFzqBAgUka2LP2SWiSQgD9teuuRgSIiIiIiYg4VAkTKiO2P6QDsCBuAl7ePyWlERERERKSqUiFApAwc3hNDm4wNuAwL9fqMMTuOiIiIiIhUYSoEiJSBQ4vfB2CzdxfqNGxuchoREREREanKVAgQKWWZOU62x58izfDE1Wmk2XFERERERKSKUyFApJQtiIljcvoQrvf8iLY9B5sdR0REREREqjgVAkRK2X9/2w/A9V1b4GG3m5xGRERERESqOhUCRErRnp2byTrwOx5WC0O71DU7joiIiIiICB5mBxCpzBJ+fJXvPL9jUfVbCPa/1uw4IiIiIiIi6hEgUlpOJZ+k1fFFANTudJ3JaURERERERHKpECBSSrb9NAMfSyYHLOG06q7eACIiIiIiUj6oECBSSvy3fw7AoYY3YbHqR01ERERERMoHnZ2IlIID29fRLHsH2YaNJlffZXYcERERERERNxUCRErBkWUfAhDjG0mtsHompxEREREREfmbCgEiJSwr20lg/BoArJ2GmZxGREREREQkPz0+UKSE/bTjKPdnPEt/vx281mOg2XFERERERETyUY8AkRL2xbqDOLFRu8v1eNjtZscRERERERHJR4UAkRIUe+w4q3bFAXBz57ompxERERERESlIhQCREnTguxf41XE/E0M3UD/I1+w4IiIiIiIiBagQIFJCnDk5NDw4lxBLIp0jwsyOIyIiIiIiUigVAkRKyLaVcwnhBIn40eaqW82OIyIiIiIiUig9NUAuiOFysWvjL5yI+RHH8W186n0HBwilho+D9j7H6e57iCbdbsA/KNjsqGUm5/cZAGwP7kekt4/JaURERERERAqnQoAUS1pqEpu/e4d6uz6lqXHUPfz541fwh+EFQDPbt3Syf0nW2sfYUO1S/HtPIKL95WZFLhNJJ+JolfIrWCC4xyiz44iIiIiIiJyVCgFSJIbLxcZFM6iz5mm6cRKANMOTHX5dyQppz73NrsLwDeZkWha+Wzbx16H6NHLtp2Pqcpi7nA0/XUG929+jZmg9k9ekdOz8KZpLLE522xoT0bqr2XFERERERETOSoUAOa/UzBw+/OxTHjr0EACxlhAOtryXttfeS0dfv4ITXPIv4F/s2byKhJ/eoFPST3RMXU7S1G5s6PocHa8ZWbYrUAaq7/ofAMcaDSTC5CwiIiIiIiLnokKAnNP+E6e4a8bv7Doagr/9WlrWD6fDbc8Q7n3+R+M1btudxm27sztmNcwdQ4RzD/GrZvEykTwS1Ryb1VIGa1D69hxL5eFTwxjssYJre48wO46IiIiIiMg5qRAgZ7Vvx0bu/N8B9qbaCPH3pP1t79OpfvVizyeiTSTZzVbz02fP8fCu9qQt/4sDCem8NbQ9dlvFf3DF1+sPsdloTK3GkQwLqWN2HBERERERkXOq+GdhUir2bltHwBf9eTnrOdqF2Jk39rILKgLksTs86T3qWV4c2g27zcL8mFj+O+V5MjPSSjB12XO6DOb8cRiAgR1VBBARERERkfJPhQApIP7QHnxn30x1kqnucPHpsHYE+3uVyLxvaF+bD4Z15lHH/7jz+Gtse28IrpycEpm3Gbatms+4U+9wmddfXNWi6jwqUUREREREKi4VAs7iP//5Dw0aNMDLy4uuXbuydu1asyOViZSkBNI+GUgwCey31iV49HwCgkJKdBm9mgVzee8byTI86JD6C2s/GA2GUaLLKCtZa6czxGMZY4PW42W3mR1HRERERETkvFQIKMSXX37J+PHjeeqpp9iwYQPt2rUjKiqKo0ePmh2tVBkuF7s+GE5D1z6OE4hj+NclXgTI06bHDWzu8hIA3Y7OZu2XL5XKckpTanICLZN+ASCw+3CT04iIiIiIiBSNCgGFeOONN7j77ru58847admyJVOnTsXHx4dPPvnE7Gilas2XL9Lx1C9kGTYSrp9BWP1mpbq8ztfdzepGDwDQfvur7Px9Sakur6Rt/+lTvC1Z7LfUoVmHK8yOIyIiIiIiUiQqBPxDVlYW69evp3fv3u5hVquV3r17s3r16kKnyczMJDk5Od+rotm05xBNdkwFYEPzR2jasWeZLLfb7U+z3u8KHBYnAd/fQ8LR2DJZbknw2/EVALENBmCx6kdJREREREQqBp29/MPx48dxOp2EhOTvEh8SEkJcXFyh07z44osEBAS4X3Xr1i2LqCXqx92pDMh6mh9q3E7XIY+V2XItVitN747moCWcWsYJ5nw1A6MC3C8gdu92WmRtwWVYaHjlSLPjiIiIiIiIFJkKASXg8ccfJykpyf06ePCg2ZGKbUJUcx4d2pfL7nmrzL/drhZQg8xBMxjqfJpnD7blmw2Hy3T5F+LA0ukAbPXqQGjdxianERERERERKToVAv6hZs2a2Gw24uPj8w2Pj48nNDS00Gk8PT3x9/fP96qI+rcLx9/LbsqyI1pfwhVXXgvA5HlbiU1MNyVHURiGwW/xsN8VTHqLwWbHERERERERKRYVAv7B4XDQqVMnliz5+8Z1LpeLJUuWEBkZaWKyyu++KxrTvm4gwZn72fjJgxgul9mRCrU1Npm3kq6gj+ttWvTRZQEiIiIiIlKxeJgdoDwaP348w4cPp3PnzlxyySW89dZbnDp1ijvvvNPsaJWah83K6zc0otaHg/FPTmPjT5fTvs8dZscq4LtNuTc07N0ilGo+3ianERERERERKR71CCjEkCFDeO2115g0aRLt27dn48aNLFy4sMANBKXkNa4TxpY6QwAIXfU0aalJJifKz5mTw4kN3+Egm+vbh5sdR0REREREpNgsRkW4RXsFk5ycTEBAAElJSRX2fgFmSjuVTPKrHQnlGKvDRxB5z9tmR3LbunIerX66nYOEEPzENjzt6lQjIiIiIiLmK855qHoESLnj4+vPke6TAeh0+L8c2bfD3EBnSFv/OQCx1S9REUBERERERCokFQKkXGrf+1ZiHO1xWHKI/eZJs+MAkJmRRrOTSwHw7TzU5DQiIiIiIiIXRoUAKZcsVite1z4HQKfkxeze9KvJiWDbL9/gTxpHqUGLbteYHUdEREREROSCqG+zlFtN2l/OL8sGsOKYD3vWufiknbl5jM2zAdgT0pdgm83cMCIiIiIiIhdIPQKkXGs4bArR9Ofn3cms2nPctBypyQm0SlkFQM3I203LISIiIiIicrFUCJByrW4NH265pB4A7/z0p2k5tqz4Dk9LNvutdYhoG2laDhERERERkYulQoCUe6N7NuZaj/U8dfhetq36wZQMU+Jb0jvzFda1fBKLVT82IiIiIiJScemMRsq9sABvhofsoYX1AK7lL5f58o+nZrJy93F2G3Xo1PP6Ml++iIiIiIhISVIhQCqEetc/QZZho3XmRnas+bFMl71gcyxOl0G7OgE0rOlbpssWEREREREpaSoESIUQVr8pG4NyH9mXtfSVMl126+V38579HW6LyC7T5YqIiIiIiJQGFQKkwgjv9y+choW2Ges4sH1dmSzzeOw+2mX8znW237i8Re0yWaaIiIiIiEhpUiFAKow6jVux0e8yAOJ/fLNMlrnnl1lYLQY7PFoQVr9pmSxTRERERESkNKkQIBWKzxUPAtAuYRHH4w6W+vL893wPwMmG/Up9WSIiIiIiImVBhQCpUJp36c33Xv0Znf0gn24+VarLOnZ4Ly2ytwLQsMetpbosERERERGRsqJCgFQoFosFrn2FJa5O/HfNQTKynaW2rL9+mQnAdntLQus2LrXliIiIiIiIlCUVAqTC6dsqlNqB3iScyuK7TbGltpyAv+YDkNjwulJbhoiIiIiISFlTIUAqHA+blZGdAnjI43/U/3FUqSwjLjGdOWnt2OxqSKMrbimVZYiIiIiIiJjBw+wAIhdiQLswqq38DkdWDrv/+IWIDj1KdP4/bIljmrM/6+sM43+1G5XovEVERERERMykHgFSIQUFh7PJvycACb9MLfH5/xBzBIB+bcNKfN4iIiIiIiJmUiFAKiy/y+4FoE3CYpJOHi+x+cYf3kvtg/PwI41rWqsQICIiIiIilYsKAVJhNe/Sm73W+nhbsti+cFqJzXffss942/E+n/u/S2iAV4nNV0REREREpDxQIUAqLIvVytFmtwEQtutzDJerROZbfe/3AJxqdE2JzE9ERERERKQ8USFAKrSWfe8mzfCkvusguzYsvej5xR/aTdOcnbgMC4173FoCCUVERERERMoXFQKkQqsWUIPfa/RjVs6VfL8746Lnt3fFbAB2OlpSK7z+Rc9PRERERESkvFEhQCo8W79X+VfOXUzf4UFGtvOi5lVt7wIAkhr0LYloIiIiIiIi5Y4KAVLhRTYKonagNykZOfy4Lf6C53PyWCzNM2MAqHfpkJKKJyIiIiIiUq6oECAVntVqYVDH2nSw7CJ56TsXPJ9tvy3CZjHYZWtMeINmJZhQRERERESk/FAhQCqFIU1gjudT3HpyKvGH9lzQPD450ZpuGe+yvs1TJZxORERERESk/FAhQCqF2g2bsc3RBqvF4K8lHxd7+tTMHFbsOk4cQXSMvLIUEoqIiIiIiJQPKgRIpZHa4mYAau//FgyjWNMu2xFPltNFw5q+NAn2K414IiIiIiIi5YIKAVJpNOt1O5mGnXquQ+zd+luxpg3+6UFm2F9iRP3jWCyWUkooIiIiIiJiPhUCpNIICKzBVt9uAMSvmlXk6TLST9EqeQVX2DbTrXGt0oonIiIiIiJSLqgQIJWK0WYQAPWPLMBwuYo0zc5V8/C1ZBBPEE3aXV6a8UREREREREynQoBUKi173MQpwwtcTmK2by/SNFkx3wKwt1YvrDb9SIiIiIiISOWmsx6pVLx9/Xi70Qd0z3yHb4rwFMGc7CyaJP4CgF/7G0s5nYiIiIiIiPkqTCHg+eefp3v37vj4+BAYGFhomwMHDtCvXz98fHwIDg5mwoQJ5OTk5GuzbNkyOnbsiKenJxEREURHRxeYz3/+8x8aNGiAl5cXXbt2Ze3ataWwRlJaunXthoGV7zcfIcd57ssDdq5ZRCCpnKQazS/pU0YJRUREREREzFNhCgFZWVncdNNNjB49utDxTqeTfv36kZWVxapVq5gxYwbR0dFMmjTJ3Wbv3r3069ePXr16sXHjRsaNG8ddd93FokWL3G2+/PJLxo8fz1NPPcWGDRto164dUVFRHD16tNTXUUrGZRG1CPSxczI1jXXb956zbeqmuQDsCrwcD7ujDNKJiIiIiIiYy2IYxXzgusmio6MZN24ciYmJ+YYvWLCA6667jtjYWEJCQgCYOnUqEydO5NixYzgcDiZOnMj8+fPZsmWLe7qhQ4eSmJjIwoULAejatStdunThvffeA8DlclG3bl3uv/9+HnvssSJlTE5OJiAggKSkJPz9/UtgraW4Pv/kTXrvf4N9NS6ny4OFP0HAMAyefX4SvTJ/xq/nODpcdVMZpxQRERERESkZxTkPrTA9As5n9erVtGnTxl0EAIiKiiI5OZmtW7e62/Tu3TvfdFFRUaxevRrI7XWwfv36fG2sViu9e/d2t5GKoVWTxtSyJBNxcgXOnOxC22w7kswnqd24h3/TosfAMk4oIiIiIiJijkpTCIiLi8tXBADc7+Pi4s7ZJjk5mfT0dI4fP47T6Sy0Td48CpOZmUlycnK+l5irRbe+JOJHdZLZufbHQtv8tC33co/LmtTEy24ry3giIiIiIiKmMbUQ8Nhjj2GxWM752rFjh5kRi+TFF18kICDA/apbt67Zkao8u93Bn4E9AEj545tC26Rv+ppanOTqFiGFjhcREREREamMPMxc+MMPP8yIESPO2aZRo0ZFmldoaGiBu/vHx8e7x+X9mzfszDb+/v54e3tjs9mw2WyFtsmbR2Eef/xxxo8f736fnJysYkA5YG99A6z8gYbHfsZwObFY//7W/+ihv3gs5UUe8bSS2KD8F5tERERERERKiqk9AmrVqkXz5s3P+XI4inYn98jISGJiYvLd3X/x4sX4+/vTsmVLd5slS5bkm27x4sVERkYC4HA46NSpU742LpeLJUuWuNsUxtPTE39//3wvMV+LS/tzyvAimAR2bVyRb9y+1V8DsNvejJq11CNARERERESqjgpzj4ADBw6wceNGDhw4gNPpZOPGjWzcuJHU1FQA+vTpQ8uWLbnjjjvYtGkTixYt4sknn2TMmDF4enoCcN999/HXX3/x6KOPsmPHDt5//31mz57NQw895F7O+PHj+fDDD5kxYwbbt29n9OjRnDp1ijvvvNOU9ZYL5+Xty3b/3ALOiXVf5xvn+VfufQNO1r2qzHOJiIiIiIiYydRLA4pj0qRJzJgxw/2+Q4cOACx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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Get the merger frequency to check the Planck taper window\n", + "\n", + "l1, l2 = lambda1, lambda2\n", + "merger_params = jnp.array([m1, m2, chi1, chi2, l1, l2])\n", + "\n", + "f_merger = f_merger_ripple\n", + "f_merger = float(f_merger)\n", + "\n", + "## Get amplitude and angle for comparison\n", + "\n", + "A_lalsuite = jnp.abs(hp_lalsuite)\n", + "angle_lalsuite = np.unwrap(np.angle(hp_lalsuite))\n", + "phase_lalsuite = hp_lalsuite / A_lalsuite\n", + "\n", + "A_ripple = jnp.abs(hp_ripple)\n", + "angle_ripple = np.unwrap(np.angle(hp_ripple))\n", + "phase_ripple = hp_ripple / A_ripple\n", + "\n", + "# Choose whether we plot the angle or the phase\n", + "plot_angle = True\n", + "\n", + "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", + "plt.subplot(2, 1, 1)\n", + "\n", + "# Plot the amplitude\n", + "plt.plot(freqs_lal[mask_lal], A_lalsuite, \"-\", label=\"LAL\")\n", + "plt.plot(fs_ripple, A_ripple, \"--\", label=\"ripple\")\n", + "\n", + "# Plot where the Planck taper has to take place\n", + "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", + "\n", + "plt.title(\n", + " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "plt.ylim(1e-27, 1e-21)\n", + "plt.xlim(f_l - 5)\n", + "plt.legend()\n", + "\n", + "# Plot the angle or the phase\n", + "plt.subplot(2, 1, 2)\n", + "if plot_angle:\n", + " plt.plot(freqs_lal[mask_lal], angle_lalsuite, \"-\", label=\"LAL\")\n", + " plt.plot(fs_ripple, angle_ripple, \"--\", label=\"ripple\")\n", + " name = \"Angle\"\n", + "else:\n", + " plt.plot(freqs_lal[mask_lal], phase_lalsuite, \"-\", label=\"LAL\")\n", + " plt.plot(fs_ripple, phase_ripple, \"--\", label=\"ripple\")\n", + " name = \"Phase\"\n", + "plt.legend()\n", + "plt.title(\n", + " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7146f064", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", + "plt.subplot(2, 1, 1)\n", + "\n", + "# Plot the amplitude\n", + "diffs_amplitude = abs(A_lalsuite - A_ripple) / A_lalsuite\n", + "plt.plot(f, diffs_amplitude, \"-\", color=\"black\")\n", + "\n", + "# Plot where the Planck taper has to take place\n", + "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", + "\n", + "plt.title(\n", + " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(\"Amplitude\")\n", + "# plt.ylim(1e-27, 1e-21)\n", + "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", + "# plt.legend()\n", + "\n", + "# Plot the angle or the phase\n", + "plt.subplot(2, 1, 2)\n", + "\n", + "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\")\n", + "name = \"Angle\"\n", + "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", + "# plt.legend()\n", + "plt.title(\n", + " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bc30bd23", + "metadata": {}, + "outputs": [], + "source": [ + "A_lalsuiteXAS = jnp.abs(hpXAS_lalsuite)\n", + "angle_lalsuiteXAS = np.unwrap(np.angle(hpXAS_lalsuite))\n", + "phase_lalsuiteXAS = hpXAS_lalsuite / A_lalsuiteXAS\n", + "\n", + "spin_corr_LAL = angle_lalsuite - angle_lalsuiteXAS\n", + "spin_corr_ripple = get_spin_phase_correction(M_omega**(2/3), theta_intrinsic)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ea95f1b6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/IPython/core/pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 5))\n", + "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\", label='Total error')\n", + "plt.plot(f, abs((spin_corr_LAL - spin_corr_ripple)/spin_corr_LAL), label=r\"$|\\Delta \\psi_S|$\")\n", + "plt.plot(fs, abs(delta_psi_tidal), \"-\", label=r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", + "plt.legend()\n", + "plt.title(\n", + " r\"Angle absolute error contributions ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " m1, m2, chi1, chi2, lambda1, lambda2\n", + " )\n", + ")\n", + "plt.yscale('log')\n", + "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", + "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.xscale(\"log\")\n", + "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e0003d7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f, spin_corr_ripple)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jim_dev_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 20683b318380340e6646dc5764dc8cab13fbbce7 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Thu, 30 Oct 2025 16:55:45 +0100 Subject: [PATCH 023/474] Possible phase bug in XAS found --- src/ripplegw/waveforms/IMRPhenomXAS.py | 9 +- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 9 +- test/NRTidalv3_systematic_test.ipynb | 412 ++++++++++++++++++ test/NRTidalv3_testing.ipynb | 304 ++++++++++--- test/XAS_testing.ipynb | 60 ++- 5 files changed, 709 insertions(+), 85 deletions(-) create mode 100644 test/NRTidalv3_systematic_test.ipynb diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index b9b8c959..44f8220d 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -1324,6 +1324,7 @@ def _gen_IMRPhenomXAS( phase_coeffs: Array, amp_coeffs: Array, f_ref: float, + get_phase: bool = False ): m1, m2, chi1, chi2 = theta_intrinsic m1_s = m1 * gt @@ -1359,12 +1360,16 @@ def _gen_IMRPhenomXAS( ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib + # DEBUG FEATURE + if get_phase: + return Psi, lina, linb, fM_s, phifRef, psi4tostrain + A = Amp(f, theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) h0 = A * jnp.exp(1j * Psi) return h0 -def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float): +def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float, get_phase: bool = False): """ Generate PhenomXAS frequency domain waveform following 2001.11412. Note that this waveform also assumes that object one is the more massive. @@ -1390,7 +1395,7 @@ def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float): amp_coeffs = IMRPhenomX_utils.PhenomX_amp_coeff_table h0 = _gen_IMRPhenomXAS( - f, theta_intrinsic, theta_extrinsic, phase_coeffs, amp_coeffs, f_ref + f, theta_intrinsic, theta_extrinsic, phase_coeffs, amp_coeffs, f_ref, get_phase=get_phase ) return h0 diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index ac7eb972..0bd55f5d 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -22,6 +22,7 @@ def _gen_IMRPhenomXAS_NRTidalv3( bbh_amp: Array, bbh_psi: Array, no_taper: bool = False, + get_phase: bool = False ): """ Master internal function to get the GW strain for given parameters. The function takes @@ -64,6 +65,8 @@ def _gen_IMRPhenomXAS_NRTidalv3( NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) + # TODO: add tidal phase offset + # Check for local minimum TODO fHzmrgcheck = 0.9 * f_merger increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) @@ -73,6 +76,9 @@ def _gen_IMRPhenomXAS_NRTidalv3( psi_T = NRTidalv3_phase * (1 - A_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * A_P psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) + if get_phase: # purely for debugging purposes + return bbh_psi + psi_T + psi_SS + # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) h0 = AA_P * (bbh_amp + A_T) * jnp.exp(1.0j * (bbh_psi + psi_T + psi_SS)) @@ -85,6 +91,7 @@ def gen_IMRPhenomXAS_NRTidalv3( f_ref: float, use_lambda_tildes: bool = True, no_taper: bool = False, + get_phase: bool = False ) -> Array: """ Generate NRTidalv3 frequency domain waveform following NRTidalv3 paper. @@ -164,7 +171,7 @@ def gen_IMRPhenomXAS_NRTidalv3( # Use BBH waveform and add tidal corrections return _gen_IMRPhenomXAS_NRTidalv3( - f, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper + f, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper, get_phase=get_phase ) diff --git a/test/NRTidalv3_systematic_test.ipynb b/test/NRTidalv3_systematic_test.ipynb new file mode 100644 index 00000000..a2486086 --- /dev/null +++ b/test/NRTidalv3_systematic_test.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 111, + "id": "5eee29c4", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from ripplegw.constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C\n", + "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc, gen_IMRPhenomXAS_NRTidalv3\n", + "from ripplegw import ms_to_Mc_eta, lambdas_to_lambda_tildes, get_eff_pads, get_match_arr\n", + "from ripplegw.waveforms.NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper\n", + "from ripplegw.waveforms.IMRPhenomD_NRTidalv2 import get_planck_taper, get_tidal_amplitude, get_spin_phase_correction\n", + "from ripplegw.waveforms.IMRPhenomXAS import Phase, gen_IMRPhenomXAS\n", + "from ripplegw.waveforms.IMRPhenom_tidal_utils import get_kappa\n", + "\n", + "import lalsimulation as lalsim\n", + "import lal\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "d1a9f328", + "metadata": {}, + "outputs": [], + "source": [ + "# Frequency grid\n", + "T = 128\n", + "f_l = 20.0\n", + "f_sampling = 2 * 4096\n", + "f_u = f_sampling // 2\n", + "f_ref = f_l\n", + "\n", + "delta_t = 1 / f_sampling\n", + "tlen = int(round(T / delta_t))\n", + "freqs = np.fft.rfftfreq(tlen, delta_t)\n", + "df = freqs[1] - freqs[0]\n", + "fs = freqs[(freqs > f_l) & (freqs < f_u)]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "9165e1ae", + "metadata": {}, + "outputs": [], + "source": [ + "m1 = 1.37 # In solar masses\n", + "m2 = 1.37\n", + "chi1 = 0.141 # Dimensionless spin\n", + "chi2 = 0.141\n", + "lambda1 = 1001.8\n", + "lambda2 = 1001.8\n", + "tc = 0.0 # Time of coalescence in seconds\n", + "phic = 0.0 # Time of coalescence\n", + "dist_mpc = 440 # Distance to source in Mpc\n", + "inclination = 0.0 # Inclination Angle\n", + "\n", + "q = m1/m2\n", + "Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", + "lambda_tilde, delta_lambda_tilde = lambdas_to_lambda_tildes(\n", + " jnp.array([lambda1, lambda2, m1, m2])\n", + ")\n", + "\n", + "theta_ripple = jnp.array(\n", + " [\n", + " Mc,\n", + " eta,\n", + " chi1,\n", + " chi2,\n", + " dist_mpc,\n", + " tc,\n", + " phic,\n", + " inclination,\n", + " ]\n", + ")\n", + "fs_ripple = jnp.arange(f_l, f_u, df)[1:]" + ] + }, + { + "cell_type": "markdown", + "id": "5e9166a4", + "metadata": {}, + "source": [ + "### BBH baseline check" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "85d5ced5", + "metadata": {}, + "outputs": [], + "source": [ + "# from ripplegw.waveforms import IMRPhenomX_utils\n", + "XAS_angle_ripple, lina, linb, fM_s, phifRef, psi4tostrain = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref, get_phase=True)\n", + "# XAS_angle_ripple = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "598640a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "305.656\n", + "[0.00027002 0.00027013 0.00027023 ... 0.05527867 0.05527878 0.05527889]\n", + "12529.15\n", + "9.4438925\n" + ] + } + ], + "source": [ + "print(lina)\n", + "print(linb)\n", + "print(fM_s)\n", + "print(phifRef)\n", + "print(psi4tostrain)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "bc4facc1", + "metadata": {}, + "outputs": [], + "source": [ + "theta = np.array(\n", + " [m1, m2, chi1, chi2, dist_mpc, tc, phic, inclination]\n", + ")\n", + "\n", + "m1_kg = theta[0] * lal.MSUN_SI\n", + "m2_kg = theta[1] * lal.MSUN_SI\n", + "distance = dist_mpc * 1e6 * lal.PC_SI\n", + "\n", + "laldict_OnlyPhase = lal.CreateDict()\n", + "\n", + "# Specify we only want the phase contribution. This exits the function before any NRTidal contributions so don't use this to test tidal phase contributions!\n", + "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", + "\n", + "LAL_OnlyPhase = lalsim.SimIMRPhenomXASGenerateFD(\n", + " m1_kg,\n", + " m2_kg,\n", + " chi1,\n", + " chi2,\n", + " distance,\n", + " f_l,\n", + " f_u,\n", + " df,\n", + " phic,\n", + " f_ref,\n", + " laldict_OnlyPhase,\n", + ")\n", + "\n", + "freqs_lal = np.arange(len(LAL_OnlyPhase.data.data)) * df\n", + "\n", + "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", + "XAS_angle_LAL = LAL_OnlyPhase.data.data[mask_lal]\n", + "f = freqs_lal[mask_lal]" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "8d8d22bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3.1413274+0j)\n" + ] + } + ], + "source": [ + "offset = XAS_angle_LAL[0] - XAS_angle_ripple[0]\n", + "print(offset)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "bbcbfb62", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 5))\n", + "plt.plot(f, abs((XAS_angle_LAL - (XAS_angle_ripple))), \"-\", color=\"black\")\n", + "name = \"Angle\"\n", + "# plt.legend()\n", + "plt.title(\n", + " r\"{} $h22$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "5c0b494e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 5))\n", + "plt.plot(f, XAS_angle_LAL, label='LAL')\n", + "plt.plot(f, XAS_angle_ripple, label='ripple', ls='--')\n", + "# plt.plot(f, fM_s*linb, label=r'linb $fM_s$')\n", + "# plt.plot(f, jnp.full_like(f, phifRef), label=r'$\\phi_{f_{ref}}$')\n", + "name = \"Angle\"\n", + "plt.legend()\n", + "plt.title(\n", + " r\"{} h22 ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", + " name, m1, m2, chi1, chi2\n", + " )\n", + ")\n", + "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(f\"{name}\")\n", + "plt.xscale(\"log\")\n", + "# plt.yscale(\"log\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "57b1d99c", + "metadata": {}, + "outputs": [], + "source": [ + "N = 5\n", + "rng = np.random.default_rng(seed=42)\n", + "masses = rng.uniform(1.2, 30, (N,2))\n", + "spins = rng.uniform(0, 0.8, (N,2))" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "45318f35", + "metadata": {}, + "outputs": [], + "source": [ + "offsets = np.zeros(N**2)\n", + "ctr = 0 #yes I'm lazy\n", + "\n", + "for i, ms in enumerate(masses):\n", + " for j, ss in enumerate(spins):\n", + " m1, m2 = ms\n", + " chi1, chi2 = ss\n", + "\n", + " tc = 0.0 # Time of coalescence in seconds\n", + " phic = 0.0 # Time of coalescence\n", + " dist_mpc = 440 # Distance to source in Mpc -> don't really matter here\n", + " inclination = 0.0 # Inclination Angle -> don't really matter here\n", + "\n", + " q = m1/m2\n", + " Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", + " theta_ripple = jnp.array(\n", + " [\n", + " Mc,\n", + " eta,\n", + " chi1,\n", + " chi2,\n", + " dist_mpc,\n", + " tc,\n", + " phic,\n", + " inclination,\n", + " ]\n", + " )\n", + " \n", + " ### ripple computation ###\n", + " XAS_angle_ripple, lina, linb, fM_s, phifRef, psi4tostrain = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref, get_phase=True)\n", + "\n", + "\n", + " ### LAL computation ###\n", + " m1_kg = m1 * lal.MSUN_SI\n", + " m2_kg = m2 * lal.MSUN_SI\n", + " distance = dist_mpc * 1e6 * lal.PC_SI\n", + "\n", + " laldict_OnlyPhase = lal.CreateDict()\n", + "\n", + " # Specify we only want the phase contribution. This exits the function before any NRTidal contributions so don't use this to test tidal phase contributions!\n", + " lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", + "\n", + " LAL_OnlyPhase = lalsim.SimIMRPhenomXASGenerateFD(\n", + " m1_kg,\n", + " m2_kg,\n", + " chi1,\n", + " chi2,\n", + " distance,\n", + " f_l,\n", + " f_u,\n", + " df,\n", + " phic,\n", + " f_ref,\n", + " laldict_OnlyPhase,\n", + " )\n", + " XAS_angle_LAL = LAL_OnlyPhase.data.data[mask_lal]\n", + "\n", + " offset = XAS_angle_LAL[0] - XAS_angle_ripple[0]\n", + " offsets[ctr] += offset.real\n", + " ctr += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "bd500d45", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(np.arange(N**2), offsets, edgecolor='blue', alpha=0.5, label='data')\n", + "plt.axhline(PI, 0, N**2, ls='--', c='r', label=r'$\\pi$')\n", + "plt.ylabel(r'$\\Delta \\psi$')\n", + "plt.title(r'XAS $h_{22}$-mode phase offset')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2266917", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jim_dev_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test/NRTidalv3_testing.ipynb b/test/NRTidalv3_testing.ipynb index d49f341c..b1d497e9 100644 --- a/test/NRTidalv3_testing.ipynb +++ b/test/NRTidalv3_testing.ipynb @@ -2,40 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 66, "id": "22705cfa", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", - "\n", - "SWIGLAL standard output/error redirection is enabled in IPython.\n", - "This may lead to performance penalties. To disable locally, use:\n", - "\n", - "with lal.no_swig_redirect_standard_output_error():\n", - " ...\n", - "\n", - "To disable globally, use:\n", - "\n", - "lal.swig_redirect_standard_output_error(False)\n", - "\n", - "Note however that this will likely lead to error messages from\n", - "LAL functions being either misdirected or lost when called from\n", - "Jupyter notebooks.\n", - "\n", - "To suppress this warning, use:\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", - "import lal\n", - "\n", - " import lal\n" - ] - } - ], + "outputs": [], "source": [ "import jax\n", "import jax.numpy as jnp\n", @@ -52,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 67, "id": "7bd3da0b", "metadata": {}, "outputs": [], @@ -73,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 68, "id": "3f33f2db", "metadata": {}, "outputs": [], @@ -114,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 69, "id": "a330c909", "metadata": {}, "outputs": [], @@ -127,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 70, "id": "a65d3d67", "metadata": {}, "outputs": [ @@ -149,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 71, "id": "c1dbdc6b", "metadata": {}, "outputs": [], @@ -170,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 72, "id": "610906ab", "metadata": {}, "outputs": [ @@ -205,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 73, "id": "f842e550", "metadata": {}, "outputs": [ @@ -232,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 74, "id": "9d8657b1", "metadata": {}, "outputs": [ @@ -260,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 75, "id": "d943c569", "metadata": {}, "outputs": [ @@ -270,7 +240,7 @@ "0" ] }, - "execution_count": 10, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -320,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 76, "id": "fcfeb8b7", "metadata": {}, "outputs": [ @@ -357,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 77, "id": "8af9d8b5", "metadata": {}, "outputs": [ @@ -407,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 78, "id": "bd08c356", "metadata": {}, "outputs": [ @@ -419,7 +389,7 @@ " 0.0000000e+00+0.0000000e+00j, 0.0000000e+00+0.0000000e+00j], dtype=complex64)" ] }, - "execution_count": 13, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -434,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 79, "id": "5616c838", "metadata": {}, "outputs": [], @@ -496,7 +466,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, + "id": "60d5b5e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, "id": "05c1e340", "metadata": {}, "outputs": [], @@ -506,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 81, "id": "0f7b849c", "metadata": {}, "outputs": [ @@ -543,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 82, "id": "5940b64e", "metadata": {}, "outputs": [ @@ -629,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 83, "id": "7146f064", "metadata": {}, "outputs": [ @@ -692,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 84, "id": "bc30bd23", "metadata": {}, "outputs": [], @@ -701,13 +679,51 @@ "angle_lalsuiteXAS = np.unwrap(np.angle(hpXAS_lalsuite))\n", "phase_lalsuiteXAS = hpXAS_lalsuite / A_lalsuiteXAS\n", "\n", - "spin_corr_LAL = angle_lalsuite - angle_lalsuiteXAS\n", + "spin_corr_LAL = angle_lalsuite - angle_lalsuiteXAS - ph_tdLAL\n", "spin_corr_ripple = get_spin_phase_correction(M_omega**(2/3), theta_intrinsic)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 85, + "id": "a24ff196", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f, spin_corr_LAL)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88959ee7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "929d406f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, "id": "ea95f1b6", "metadata": {}, "outputs": [ @@ -721,7 +737,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -732,12 +748,12 @@ ], "source": [ "plt.figure(figsize=(12, 5))\n", - "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\", label='Total error')\n", - "plt.plot(f, abs((spin_corr_LAL - spin_corr_ripple)/spin_corr_LAL), label=r\"$|\\Delta \\psi_S|$\")\n", - "plt.plot(fs, abs(delta_psi_tidal), \"-\", label=r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", + "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\", label='Total relative error')\n", + "plt.plot(f, abs((spin_corr_LAL - spin_corr_ripple)/spin_corr_LAL), label=r\"$|\\Delta \\psi_S / \\psi_S^{LAL}|$\")\n", + "plt.plot(fs, abs(delta_psi_tidal/ph_tdLAL), \"-\", label=r\"$|\\Delta \\psi^{NRT3a}_T / \\psi_T^{LAL}|$\")\n", "plt.legend()\n", "plt.title(\n", - " r\"Angle absolute error contributions ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " r\"Angle error contributions ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", " m1, m2, chi1, chi2, lambda1, lambda2\n", " )\n", ")\n", @@ -752,23 +768,174 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "e0003d7b", + "execution_count": 87, + "id": "da2b0f0f", + "metadata": {}, + "outputs": [], + "source": [ + "Xb = 1-Xa\n", + "SS_3p5PN, SSS_3p5PN = lalsim.SimInspiralGetHOSpinTerms(\n", + " Xa,\n", + " Xb,\n", + " chi1,\n", + " chi2,\n", + " quad1,\n", + " quad2\n", + ")\n", + "\n", + "SS_3p5PN *= 3.*M_omega**(2/3)/(128.*eta)\n", + "SSS_3p5PN *= 3.*M_omega**(2/3)/(128.*eta)" + ] + }, + { + "cell_type": "markdown", + "id": "f4f57c6e", + "metadata": {}, + "source": [ + "### Test double counting of $\\psi^{NRT3a}_T$ and $\\psi_{spin}$" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "2d659e2e", + "metadata": {}, + "outputs": [], + "source": [ + "laldict_OnlyPhase = lal.CreateDict()\n", + "lalsim.SimInspiralWaveformParamsInsertTidalLambda1(laldict_OnlyPhase, lambda1)\n", + "lalsim.SimInspiralWaveformParamsInsertTidalLambda2(laldict_OnlyPhase, lambda2)\n", + "quad1 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda1)\n", + "quad2 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda2)\n", + "# print(quad1)\n", + "oct1 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad1)\n", + "oct2 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad2)\n", + "# print(oct1)\n", + "lalsim.SimInspiralWaveformParamsInsertdQuadMon1(laldict_OnlyPhase, quad1 - 1)\n", + "lalsim.SimInspiralWaveformParamsInsertdQuadMon2(laldict_OnlyPhase, quad2 - 1)\n", + "\n", + "# Specify we only want phase contribution (before LAL seems to add tidal ans spin phase terms again)\n", + "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", + "\n", + "phaseLAL = lalsim.SimIMRPhenomXASGenerateFD(\n", + " m1_kg,\n", + " m2_kg,\n", + " chi1,\n", + " chi2,\n", + " distance,\n", + " f_l,\n", + " f_u,\n", + " df,\n", + " phic,\n", + " f_ref,\n", + " laldict_OnlyPhase,\n", + ")\n", + "\n", + "phaseLAL = phaseLAL.data.data[mask_lal]\n", + "\n", + "h22 = lalsim.SimIMRPhenomXASGenerateFD(\n", + " m1_kg,\n", + " m2_kg,\n", + " chi1,\n", + " chi2,\n", + " distance,\n", + " f_l,\n", + " f_u,\n", + " df,\n", + " phic,\n", + " f_ref,\n", + " laldict,\n", + ")\n", + "\n", + "h22 = h22.data.data[mask_lal]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "bcd5c320", + "metadata": {}, + "outputs": [], + "source": [ + "A_h22 = jnp.abs(h22)\n", + "angle_h22 = np.unwrap(np.angle(h22))\n", + "phase_h22 = h22 / A_h22" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "92c27e7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8.95752162e+00+0.j 1.71211590e+01+0.j 2.52763224e+01+0.j ...\n", + " 1.25424578e+04+0.j 1.25424578e+04+0.j 1.25424578e+04+0.j]\n", + "[2.67433632e+00 4.55478840e+00 6.42676652e+00 ... 5.58697168e+03\n", + " 5.58697168e+03 5.58697168e+03]\n" + ] + } + ], + "source": [ + "print(phaseLAL)\n", + "print(angle_h22)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "09d0b96c", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", 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" ] }, - "execution_count": 21, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f, phaseLAL.real)\n", + "plt.plot(f, angle_h22.real)\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c960ccb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "1bc242c1", + "metadata": {}, + "outputs": [], + "source": [ + "angle_rp = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "37a2cbc6", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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iBgAAWIW4AQAAViFuAACAVYgbAABgFeIGAABYhbgBAABWIW4AAIBViBsAAGAV4gYAAFiFuAEAAFYhbgAAgFWIGwAAYBXiBgAAWIW4AQAAViFuAACAVYgbAABgFeIGAABYhbgBAABWIW4AAIBViBsAAGAV4gYAAFiFuAEAAFYhbgAAgFWIGwAAYBXiBgAAWIW4AQAAViFuAACAVYgbAABgFeIGAABYhbgBAABWIW4AAIBViBsAAGAV4gYAAFiFuAEAAFYhbgAAgFWIGwAAYBXiBgAAWIW4AQAAViFuAACAVYgbAABglZjFzfHjxzVp0iR5PB6lpqZq6tSpOnnyZKPHnDlzRjNmzFCXLl3UoUMHjR8/XhUVFc7+d955RxMnTlRmZqbatWun/v3768knn4zVCAAAIA7FLG4mTZqk3bt3q6ioSGvXrtXrr7+u6dOnN3rMT3/6U/31r3/V6tWrtWnTJh05ckS33Xabs7+kpERdu3bVCy+8oN27d+uBBx5QQUGBFi9eHKsxAABAnHEZY0xz3+nevXs1YMAAbd26VcOHD5ckrV+/XjfffLM+/PBDZWRkXHBMOBxWenq6VqxYodtvv12StG/fPvXv31/BYFAjR46s97FmzJihvXv3asOGDU0+v0gkopSUFIXDYXk8ni8xIQAAuNya+vodk3dugsGgUlNTnbCRpEAgoISEBG3evLneY0pKSlRTU6NAIOBs69evn7KyshQMBht8rHA4rM6dOzffyQMAgLiWGIs7DYVC6tq1a/QDJSaqc+fOCoVCDR6TlJSk1NTUqO1er7fBY9566y2tWrVKf/vb3xo9n+rqalVXVzt/j0QiTZgCAADEo0t652bevHlyuVyN3vbt2xerc41SWlqqW2+9VQsXLtRNN93U6NrCwkKlpKQ4t8zMzMtyjgAA4PK7pHdu5syZo7vvvrvRNb1795bP51NlZWXU9nPnzun48ePy+Xz1Hufz+XT27FlVVVVFvXtTUVFxwTF79uxRbm6upk+frgcffPCi511QUKD8/Hzn75FIhMABAMBSlxQ36enpSk9Pv+g6v9+vqqoqlZSUKDs7W5K0YcMG1dbWKicnp95jsrOz1bZtWxUXF2v8+PGSpLKyMpWXl8vv9zvrdu/ere985zuaPHmyfvWrXzXpvN1ut9xud5PWAgCA+BaTb0tJ0pgxY1RRUaFly5appqZGU6ZM0fDhw7VixQpJ0uHDh5Wbm6vnn39eI0aMkCTde++9WrdunZYvXy6Px6NZs2ZJ+uyzNdJnP4r6zne+o7y8PD322GPOY7Vp06ZJ0VWHb0sBABB/mvr6HZMPFEvSiy++qJkzZyo3N1cJCQkaP368Fi1a5OyvqalRWVmZTp8+7Wz73e9+56ytrq5WXl6enn76aWf/yy+/rKNHj+qFF17QCy+84Gzv0aOHPvjgg1iNAgAA4kjM3rlpzXjnBgCA+NOiv+cGAACgpRA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKvELG6OHz+uSZMmyePxKDU1VVOnTtXJkycbPebMmTOaMWOGunTpog4dOmj8+PGqqKiod+3HH3+sK664Qi6XS1VVVTGYAAAAxKOYxc2kSZO0e/duFRUVae3atXr99dc1ffr0Ro/56U9/qr/+9a9avXq1Nm3apCNHjui2226rd+3UqVM1ePDgWJw6AACIYy5jjGnuO927d68GDBigrVu3avjw4ZKk9evX6+abb9aHH36ojIyMC44Jh8NKT0/XihUrdPvtt0uS9u3bp/79+ysYDGrkyJHO2qVLl2rVqlVasGCBcnNz9cknnyg1NbXJ5xeJRJSSkqJwOCyPx/PVhgUAAJdFU1+/Y/LOTTAYVGpqqhM2khQIBJSQkKDNmzfXe0xJSYlqamoUCAScbf369VNWVpaCwaCzbc+ePXrkkUf0/PPPKyGhaadfXV2tSCQSdQMAAHaKSdyEQiF17do1altiYqI6d+6sUCjU4DFJSUkXvAPj9XqdY6qrqzVx4kQ99thjysrKavL5FBYWKiUlxbllZmZe2kAAACBuXFLczJs3Ty6Xq9Hbvn37YnWuKigoUP/+/fXDH/7wko8Lh8PO7dChQzE6QwAA0NISL2XxnDlzdPfddze6pnfv3vL5fKqsrIzafu7cOR0/flw+n6/e43w+n86ePauqqqqod28qKiqcYzZs2KB3331XL7/8siSp7uNCaWlpeuCBB/Twww/Xe99ut1tut7spIwIAgDh3SXGTnp6u9PT0i67z+/2qqqpSSUmJsrOzJX0WJrW1tcrJyan3mOzsbLVt21bFxcUaP368JKmsrEzl5eXy+/2SpD/96U/69NNPnWO2bt2qH/3oR3rjjTd05ZVXXsooAADAUpcUN03Vv39/jR49WtOmTdOyZctUU1OjmTNn6o477nC+KXX48GHl5ubq+eef14gRI5SSkqKpU6cqPz9fnTt3lsfj0axZs+T3+51vSn0xYI4dO+Y83qV8WwoAANgrJnEjSS+++KJmzpyp3NxcJSQkaPz48Vq0aJGzv6amRmVlZTp9+rSz7Xe/+52ztrq6Wnl5eXr66adjdYoAAMBCMfk9N60dv+cGAID406K/5wYAAKClEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCrEDQAAsApxAwAArELcAAAAqxA3AADAKsQNAACwCnEDAACsQtwAAACrEDcAAMAqxA0AALAKcQMAAKxC3AAAAKsQNwAAwCqJLX0CLcEYI0mKRCItfCYAAKCp6l63617HG/K1jJsTJ05IkjIzM1v4TAAAwKU6ceKEUlJSGtzvMhfLHwvV1tbqyJEj6tixo1wu11e+v0gkoszMTB06dEgej6cZzrB1sXk+ZotfNs/HbPHJ5tmk1jGfMUYnTpxQRkaGEhIa/mTN1/Kdm4SEBF1xxRXNfr8ej8fKJ3Qdm+djtvhl83zMFp9snk1q+fkae8emDh8oBgAAViFuAACAVYibZuB2u7Vw4UK53e6WPpWYsHk+ZotfNs/HbPHJ5tmk+Jrva/mBYgAAYC/euQEAAFYhbgAAgFWIGwAAYBXiBgAAWIW4aQZLlixRz549lZycrJycHG3ZsqWlT+mifvGLX8jlckXd+vXr5+w/c+aMZsyYoS5duqhDhw4aP368Kioqou6jvLxcY8eOVfv27dW1a1fNnTtX586du9yj6PXXX9d3v/tdZWRkyOVyac2aNVH7jTFasGCBunXrpnbt2ikQCOi9996LWnP8+HFNmjRJHo9Hqampmjp1qk6ePBm1ZteuXbr++uuVnJyszMxM/eY3v4n1aBed7e67777gOo4ePTpqTWudrbCwUNdcc406duyorl27aty4cSorK4ta01zPw40bN2rYsGFyu9266qqrtHz58haf7cYbb7zg2t1zzz2tfjZJWrp0qQYPHuz8Mje/36+///3vzv54vW5NmS2er9sXPfroo3K5XLrvvvucbfF87aIYfCUrV640SUlJ5tlnnzW7d+8206ZNM6mpqaaioqKlT61RCxcuNFdffbX56KOPnNvRo0ed/ffcc4/JzMw0xcXFZtu2bWbkyJHm2muvdfafO3fODBw40AQCAbNjxw6zbt06k5aWZgoKCi77LOvWrTMPPPCAeeWVV4wk8+qrr0btf/TRR01KSopZs2aNeeedd8wtt9xievXqZT799FNnzejRo82QIUPM22+/bd544w1z1VVXmYkTJzr7w+Gw8Xq9ZtKkSaa0tNS89NJLpl27dub3v/99i842efJkM3r06KjrePz48ag1rXW2vLw889xzz5nS0lKzc+dOc/PNN5usrCxz8uRJZ01zPA//+9//mvbt25v8/HyzZ88e89RTT5k2bdqY9evXt+hso0aNMtOmTYu6duFwuNXPZowxf/nLX8zf/vY385///MeUlZWZ+fPnm7Zt25rS0lJjTPxet6bMFs/X7fO2bNlievbsaQYPHmxmz57tbI/na/d5xM1XNGLECDNjxgzn7+fPnzcZGRmmsLCwBc/q4hYuXGiGDBlS776qqirTtm1bs3r1amfb3r17jSQTDAaNMZ+96CYkJJhQKOSsWbp0qfF4PKa6ujqm596YLwZAbW2t8fl85rHHHnO2VVVVGbfbbV566SVjjDF79uwxkszWrVudNX//+9+Ny+Uyhw8fNsYY8/TTT5tOnTpFzXb//febvn37xnii/2kobm699dYGj4mX2YwxprKy0kgymzZtMsY03/Pw5z//ubn66qujHmvChAkmLy8v1iM5vjibMZ+9SH7+ReWL4mW2Op06dTJ/+MMfrLpudepmM8aO63bixAnTp08fU1RUFDWPTdeOH0t9BWfPnlVJSYkCgYCzLSEhQYFAQMFgsAXPrGnee+89ZWRkqHfv3po0aZLKy8slSSUlJaqpqYmaq1+/fsrKynLmCgaDGjRokLxer7MmLy9PkUhEu3fvvryDNOLAgQMKhUJRs6SkpCgnJydqltTUVA0fPtxZEwgElJCQoM2bNztrbrjhBiUlJTlr8vLyVFZWpk8++eQyTVO/jRs3qmvXrurbt6/uvfdeffzxx86+eJotHA5Lkjp37iyp+Z6HwWAw6j7q1lzOf0a/OFudF198UWlpaRo4cKAKCgp0+vRpZ1+8zHb+/HmtXLlSp06dkt/vt+q6fXG2OvF+3WbMmKGxY8decA42Xbuv5X84s7kcO3ZM58+fj7rIkuT1erVv374WOqumycnJ0fLly9W3b1999NFHevjhh3X99dertLRUoVBISUlJSk1NjTrG6/UqFApJkkKhUL1z1+1rLerOpb5z/fwsXbt2jdqfmJiozp07R63p1avXBfdRt69Tp04xOf+LGT16tG677Tb16tVL77//vubPn68xY8YoGAyqTZs2cTNbbW2t7rvvPn3729/WwIEDncdujudhQ2sikYg+/fRTtWvXLhYjOeqbTZJ+8IMfqEePHsrIyNCuXbt0//33q6ysTK+88kqj5123r7E1l2O2d999V36/X2fOnFGHDh306quvasCAAdq5c2fcX7eGZpPi/7qtXLlS27dv19atWy/YZ8s/cxJx87U1ZswY58+DBw9WTk6OevTooT/+8Y+X5YmH5nHHHXc4fx40aJAGDx6sK6+8Uhs3blRubm4LntmlmTFjhkpLS/Xmm2+29Kk0u4Zmmz59uvPnQYMGqVu3bsrNzdX777+vK6+88nKf5iXr27evdu7cqXA4rJdfflmTJ0/Wpk2bWvq0mkVDsw0YMCCur9uhQ4c0e/ZsFRUVKTk5uaVPJ6b4sdRXkJaWpjZt2lzwSfKKigr5fL4WOqsvJzU1Vd/85je1f/9++Xw+nT17VlVVVVFrPj+Xz+erd+66fa1F3bk0do18Pp8qKyuj9p87d07Hjx+Pu3l79+6ttLQ07d+/X1J8zDZz5kytXbtWr732mq644gpne3M9Dxta4/F4Yh7yDc1Wn5ycHEmKunatebakpCRdddVVys7OVmFhoYYMGaInn3zSiuvW0Gz1iafrVlJSosrKSg0bNkyJiYlKTEzUpk2btGjRIiUmJsrr9cb9tatD3HwFSUlJys7OVnFxsbOttrZWxcXFUT+fjQcnT57U+++/r27duik7O1tt27aNmqusrEzl5eXOXH6/X++++27UC2dRUZE8Ho/z9m1r0KtXL/l8vqhZIpGINm/eHDVLVVWVSkpKnDUbNmxQbW2t8y8uv9+v119/XTU1Nc6aoqIi9e3bt8V+JFWfDz/8UB9//LG6desmqXXPZozRzJkz9eqrr2rDhg0X/GisuZ6Hfr8/6j7q1sTyn9GLzVafnTt3SlLUtWuNszWktrZW1dXVcX3dGlI3W33i6brl5ubq3Xff1c6dO53b8OHDNWnSJOfP1ly7y/bRZUutXLnSuN1us3z5crNnzx4zffp0k5qaGvVJ8tZozpw5ZuPGjebAgQPm3//+twkEAiYtLc1UVlYaYz77OmBWVpbZsGGD2bZtm/H7/cbv9zvH130d8KabbjI7d+4069evN+np6S3yVfATJ06YHTt2mB07dhhJ5re//a3ZsWOHOXjwoDHms6+Cp6ammj//+c9m165d5tZbb633q+Df+ta3zObNm82bb75p+vTpE/V16aqqKuP1es2dd95pSktLzcqVK0379u1j/nXpxmY7ceKE+dnPfmaCwaA5cOCA+de//mWGDRtm+vTpY86cOdPqZ7v33ntNSkqK2bhxY9TXak+fPu2saY7nYd3XUufOnWv27t1rlixZEvOvpV5stv3795tHHnnEbNu2zRw4cMD8+c9/Nr179zY33HBDq5/NGGPmzZtnNm3aZA4cOGB27dpl5s2bZ1wul/nnP/9pjInf63ax2eL9utXni9/+iudr93nETTN46qmnTFZWlklKSjIjRowwb7/9dkuf0kVNmDDBdOvWzSQlJZnu3bubCRMmmP379zv7P/30U/PjH//YdOrUybRv395873vfMx999FHUfXzwwQdmzJgxpl27diYtLc3MmTPH1NTUXO5RzGuvvWYkXXCbPHmyMeazr4M/9NBDxuv1GrfbbXJzc01ZWVnUfXz88cdm4sSJpkOHDsbj8ZgpU6aYEydORK155513zHXXXWfcbrfp3r27efTRR1t0ttOnT5ubbrrJpKenm7Zt25oePXqYadOmXRDWrXW2+uaSZJ577jlnTXM9D1977TUzdOhQk5SUZHr37h31GC0xW3l5ubnhhhtM586djdvtNldddZWZO3du1O9Laa2zGWPMj370I9OjRw+TlJRk0tPTTW5urhM2xsTvdbvYbPF+3erzxbiJ52v3eS5jjLl87xMBAADEFp+5AQAAViFuAACAVYgbAABgFeIGAABYhbgBAABWIW4AAIBViBsAAGAV4gYAAFiFuAEAAFYhbgAAgFWIGwAAYBXiBgAAWOX/AdEHex+umNfCAAAAAElFTkSuQmCC", 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", 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" ] @@ -778,7 +945,10 @@ } ], "source": [ - "plt.plot(f, spin_corr_ripple)" + "plt.plot(f, (phaseLAL.real - (angle_rp - tidal_phase_corr + PI))/phaseLAL.real)\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.show()" ] } ], diff --git a/test/XAS_testing.ipynb b/test/XAS_testing.ipynb index 1cb838e3..051fe5c1 100644 --- a/test/XAS_testing.ipynb +++ b/test/XAS_testing.ipynb @@ -2,10 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "id": "4a34eed3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(False)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], "source": [ "import jax\n", "import jax.numpy as jnp\n", @@ -22,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "id": "672ec381", "metadata": {}, "outputs": [], @@ -43,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "id": "c190d1fb", "metadata": {}, "outputs": [], @@ -77,18 +107,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "id": "166999f4", "metadata": {}, "outputs": [], "source": [ "# Generate ripple plus strain\n", - "hp_ripple, _ = gen_IMRPhenomXAS_hphc(fs_ripple, theta_ripple, f_ref)" + "hp_ripple, hc_ripple = gen_IMRPhenomXAS_hphc(fs_ripple, theta_ripple, f_ref)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "6b134673", "metadata": {}, "outputs": [], @@ -109,7 +139,7 @@ "distance = dist_mpc * 1e6 * lal.PC_SI\n", "\n", "# Generate LAL plus strain\n", - "hp, _ = lalsim.SimInspiralChooseFDWaveform(\n", + "hp, hc = lalsim.SimInspiralChooseFDWaveform(\n", " m1_kg,\n", " m2_kg,\n", " 0.0,\n", @@ -135,14 +165,14 @@ "freqs_lal = np.arange(len(hp.data.data)) * df\n", "\n", "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", - "hp_lalsuite = hp.data.data[mask_lal]\n", "hp_LAL = hp.data.data[mask_lal]\n", + "hc_LAL = hc.data.data[mask_lal]\n", "f = freqs_lal[mask_lal]" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "id": "d06ad0d3", "metadata": {}, "outputs": [ @@ -177,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "00da7dc7", "metadata": {}, "outputs": [ @@ -199,9 +229,9 @@ "\n", "## Get amplitude and angle for comparison\n", "\n", - "A_lalsuite = jnp.abs(hp_lalsuite)\n", - "angle_lalsuite = np.unwrap(np.angle(hp_lalsuite))\n", - "phase_lalsuite = hp_lalsuite / A_lalsuite\n", + "A_lalsuite = jnp.abs(hp_LAL)\n", + "angle_lalsuite = np.unwrap(np.angle(hp_LAL))\n", + "phase_lalsuite = hp_LAL / A_lalsuite\n", "\n", "A_ripple = jnp.abs(hp_ripple)\n", "angle_ripple = np.unwrap(np.angle(hp_ripple))\n", @@ -255,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 14, "id": "9e7c07f6", "metadata": {}, "outputs": [ From 66313de6817f5435eed0efe94d859516248e7a97 Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Fri, 31 Oct 2025 20:42:07 +0800 Subject: [PATCH 024/474] The XAS phase is identified --- test/test_XAS_phase.ipynb | 760 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 760 insertions(+) create mode 100644 test/test_XAS_phase.ipynb diff --git a/test/test_XAS_phase.ipynb b/test/test_XAS_phase.ipynb new file mode 100644 index 00000000..1d85e4c5 --- /dev/null +++ b/test/test_XAS_phase.ipynb @@ -0,0 +1,760 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da49b3ef", + "metadata": {}, + "source": [ + "# XAS Phase discrepancy between `ripple` and `LAL`" + ] + }, + { + "cell_type": "markdown", + "id": "599207e9", + "metadata": {}, + "source": [ + "This notebook records the investigation of the apparent phase discrepancy in the `PhenomXAS` waveform between `ripple` and `LALSimulation`.\n", + "\n", + "For the record (for anyone in the future to reproduce this), the versions of `LALSimulation` and `ripple` used are:" + ] + }, + { + "cell_type": "markdown", + "id": "0e8a1af1", + "metadata": {}, + "source": [ + "For `LALSimulation`, we have:\n", + "```python\n", + "import lalsimulation; print(lalsimulation.__version__)\n", + "> 6.2.0\n", + "```\n", + "and `ripple` is at this git hash: `20683b318380340e6646dc5764dc8cab13fbbce7`" + ] + }, + { + "cell_type": "markdown", + "id": "c1540eff", + "metadata": {}, + "source": [ + "## The issue:\n", + "\n", + "In a nutshell, the phases that one obtains from `LAL` and `ripple` are off by:\n", + "$$\n", + "\\Psi_{\\rm LAL}(f) - \\Psi_{\\rm ripple}(f) = 3 \\phi_c + \\pi\n", + "$$\n", + "\n", + "**NOTE:** As I will show later, even if this $3 \\phi_c$ is accounted for, there is still a frequency-decaying phase difference that remains largely unexplained.\n", + "\n", + "### Common ground:\n", + "Here, we call $\\Psi$ as the **final final** phase that satisfies:\n", + "$$\n", + "\\tilde{h}_+(f) = A(f) \\,\\exp(i \\Psi)\n", + "$$\n", + "\n", + "#### The case in `LALSimulation`\n", + "\n", + "Ultimately, for XAS (in `LAL`), we have:\n", + "$$\n", + "\\Psi(f) = \\pi + \\Psi_{\\rm XAS}(f) + b M f + \\phi_{\\rm ref}\n", + "$$\n", + "Notes:\n", + "* We have ignored $a = 0$ (it's called `lina` in code)\n", + "* This relation is most easily seen from [here](https://git.ligo.org/lscsoft/lalsuite/-/blob/lalsimulation-v6.2.0/lalsimulation/lib/LALSimIMRPhenomX.c?ref_type=tags#L840)\n", + "* The leading $\\pi$ comes from the $Y_{2,2}$ as noted [here in `LALSimInspiral`](https://git.ligo.org/lscsoft/lalsuite/-/blob/lalsimulation-v6.2.0/lalsimulation/lib/LALSimInspiralGeneratorLegacy.c?ref_type=tags#L2252)\n", + "\n", + "Here, we have ([this chunk](https://git.ligo.org/lscsoft/lalsuite/-/blob/lalsimulation-v6.2.0/lalsimulation/lib/LALSimIMRPhenomX.c?ref_type=tags#L825-L839)):\n", + "$$\n", + "\\Psi_{\\rm XAS}(f) = (1 / \\eta) \\times \\texttt{IMRPhenomX\\_...\\_Phase\\_22\\_AnsatzInt}(f) \n", + "$$\n", + "and (defined [here](https://git.ligo.org/lscsoft/lalsuite/-/blob/lalsimulation-v6.2.0/lalsimulation/lib/LALSimIMRPhenomX.c?ref_type=tags#L748)) \n", + "$$\n", + "\\phi_{\\rm ref} = - (\\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f_{\\rm ref}) + 2 \\phi_c + \\pi/4\n", + "$$\n", + "\n", + "Overall, we have:\n", + "$$\n", + "\\begin{align*}\n", + "\\Psi(f) &= \\pi + \\Psi_{\\rm XAS}(f) + b M f - (\\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f_{\\rm ref}) + 2 \\phi_c + \\pi/4 \\\\\n", + "&= \\pi + \\Psi_{\\rm XAS}(f) - \\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f - b M f_{\\rm ref} + 2 \\phi_c + \\pi/4\n", + "\\end{align*}\n", + "$$\n", + "Note that, however, with only the `PhenomXOnlyReturnPhase` ([here](https://git.ligo.org/lscsoft/lalsuite/-/blob/lalsimulation-v6.2.0/lalsimulation/lib/LALSimIMRPhenomX.c?ref_type=tags#L863-L865)) in `LAL`, it returns $\\Psi$ without the leading $\\pi$:\n", + "$$\n", + "\\Psi_{\\rm LAL}(f) = \\Psi(f) - \\pi = \\Psi_{\\rm XAS}(f) - \\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f - b M f_{\\rm ref} + 2 \\phi_c + \\pi/4\n", + "$$\n", + "\n", + "#### The case in `ripple`\n", + "\n", + "In ripple, we have instead ([here](https://github.com/GW-JAX-Team/ripple/blob/20683b318380340e6646dc5764dc8cab13fbbce7/src/ripplegw/waveforms/IMRPhenomXAS.py#L1361)):\n", + "$$\n", + "\\Psi(f) = \\Psi_{\\rm XAS}(f) + b M f + \\phi_{\\rm ref} - 2 \\pi + 2 \\pi f t_c - \\phi_c\n", + "$$\n", + "(this is first output returned when `get_phase` is on, in the modified branch.)\n", + "\n", + "Contribution of $t_c$ in `LAL` is typically done afterwards, so we will ignore it for now, leaving us with:\n", + "$$\n", + "\\Psi(f) = \\Psi_{\\rm XAS}(f) + b M f + \\phi_{\\rm ref} - 2 \\pi - \\phi_c\n", + "$$\n", + "where $\\phi_{\\rm ref}$ is computed as ([here](https://github.com/GW-JAX-Team/ripple/blob/20683b318380340e6646dc5764dc8cab13fbbce7/src/ripplegw/waveforms/IMRPhenomXAS.py#L1355)):\n", + "$$\n", + "\\phi_{\\rm ref} = - (\\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f_{\\rm ref}) + \\pi/4 + \\pi\n", + "$$\n", + "So overall, that becomes:\n", + "$$\n", + "\\begin{align*}\n", + "\\Psi(f) &= \\Psi_{\\rm XAS}(f) + b M f + (- (\\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f_{\\rm ref}) + \\pi/4 + \\pi) - 2 \\pi - \\phi_c \\\\\n", + "&= \\Psi_{\\rm XAS}(f) - \\Psi_{\\rm XAS}(f_{\\rm ref}) + b M f - b M f_{\\rm ref} + \\pi/4 - \\pi - \\phi_c \n", + "\\end{align*}\n", + "$$\n", + "\n", + "#### The verdict\n", + "\n", + "Finally, if one takes their difference, one should get:\n", + "$$\n", + "\\Psi_{\\rm LAL}(f) - \\Psi_{\\rm ripple}(f) = 3 \\phi_c + \\pi\n", + "$$\n", + "The rest of this notebook is to show this is indeed the case.\n", + "\n", + "The $\\pi$ contribution is relocated perhaps for convenience, but the overall phase shift by $3 \\phi_c$ seems weird to be unnoticed before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a8e758d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(False)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from ripplegw import ms_to_Mc_eta\n", + "from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc\n", + "from ripplegw.waveforms import IMRPhenomXAS as ripple_XAS\n", + "from ripplegw.waveforms.IMRPhenomX_utils import \\\n", + " PhenomX_phase_coeff_table, PhenomX_amp_coeff_table\n", + "\n", + "from lal import MSUN_SI, MTSUN_SI, PC_SI, CreateDict, TWOPI\n", + "import lalsimulation as lalsim\n", + "from lalsimulation import \\\n", + " SimIMRPhenomXASGenerateFD, \\\n", + " SimInspiralChooseFDWaveform, \\\n", + " SimInspiralGetApproximantFromString\n", + "\n", + "MPC_SI = PC_SI * 1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "31a56bfe", + "metadata": {}, + "outputs": [], + "source": [ + "duration = 128.0 # s\n", + "sampling_frequency = 8192.0 # Hz\n", + "reference_frequency = 30.0 # Hz\n", + "f_min = 20.0 # Hz\n", + "f_max = sampling_frequency / 2 # Hz\n", + "delta_t = 1.0 / sampling_frequency # s\n", + "N_times = int(duration * sampling_frequency)\n", + "freqs = np.fft.rfftfreq(N_times, delta_t)\n", + "delta_f = np.diff(freqs)[0]\n", + "\n", + "freq_mask = (freqs >= f_min) & (freqs <= f_max)\n", + "masked_freqs = freqs[freq_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "9f71eb5b", + "metadata": {}, + "outputs": [], + "source": [ + "zero_spin = True\n", + "face_on = True\n", + "\n", + "mass_1 = 2.37\n", + "mass_2 = 1.37\n", + "\n", + "total_mass = mass_1 + mass_2\n", + "mass_ratio = mass_2 / mass_1\n", + "chirp_mass, symmetric_mass_ratio = \\\n", + " ms_to_Mc_eta(jnp.array([mass_1, mass_2]))\n", + "delta = jnp.sqrt(1 - 4 * symmetric_mass_ratio)\n", + "\n", + "if zero_spin:\n", + " chi_1 = 0.0\n", + " chi_2 = 0.0\n", + "else:\n", + " chi_1 = 0.12\n", + " chi_2 = 0.14\n", + "\n", + "S_tot_r = (chi_1 + mass_ratio**2 * chi_2) / (1 + mass_ratio**2)\n", + "chi_asym = chi_1 - chi_2\n", + "\n", + "Lambda_1 = 0.0\n", + "Lambda_2 = 0.0\n", + "\n", + "t_c = 0.0 # Time of coalescence, s\n", + "phi_c = 0.7 # Phase of coalescence, rad\n", + "\n", + "luminosity_distance = 100.0 # Mpc\n", + "inclination = 0.0 if face_on else np.pi / 3 # rad" + ] + }, + { + "cell_type": "markdown", + "id": "879f3f5b", + "metadata": {}, + "source": [ + "### Get phase from `LAL` ($\\Psi_{\\rm LAL}(f)$)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "daac93f5", + "metadata": {}, + "outputs": [], + "source": [ + "phase_only_dict = CreateDict()\n", + "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(phase_only_dict, 1)\n", + "\n", + "_lal_XAS_phase = SimIMRPhenomXASGenerateFD(\n", + " mass_1 * MSUN_SI, mass_2 * MSUN_SI,\n", + " chi_1, chi_2, luminosity_distance * MPC_SI, \n", + " f_min, f_max, delta_f, phi_c, reference_frequency,\n", + " phase_only_dict\n", + ")\n", + "lal_XAS_phase = _lal_XAS_phase.data.data" + ] + }, + { + "cell_type": "markdown", + "id": "bafe4f35", + "metadata": {}, + "source": [ + "### Get phase from `ripple` ($\\Psi_{\\rm ripple}(f)$)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "87eea73d", + "metadata": {}, + "outputs": [], + "source": [ + "intrinsic_theta = jnp.array([\n", + " mass_1, mass_2, chi_1, chi_2\n", + "])\n", + "extrinsic_theta = jnp.array([\n", + " luminosity_distance, t_c, phi_c\n", + "])\n", + "phase_coeffs = PhenomX_phase_coeff_table\n", + "amplitude_coeffs = PhenomX_amp_coeff_table\n", + "\n", + "ripple_XAS_inners = ripple_XAS._gen_IMRPhenomXAS(\n", + " freqs, intrinsic_theta, extrinsic_theta,\n", + " phase_coeffs, amplitude_coeffs, reference_frequency,\n", + " get_phase=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "30d61c02", + "metadata": {}, + "source": [ + "### Recreate `ripple` phase myself" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "ce0a229a", + "metadata": {}, + "outputs": [], + "source": [ + "ripple_Psi_XAS = ripple_XAS.Phase(\n", + " freqs, intrinsic_theta, phase_coeffs\n", + ")\n", + "\n", + "## Reconstruct the Ripple `Psi(f)`\n", + "ripple_Psi, _, linb, Mfreqs, ripple_phi_ref, _ = ripple_XAS_inners\n", + "re_ripple_Psi = ripple_Psi_XAS + linb * Mfreqs + ripple_phi_ref - TWOPI - phi_c # + TWOPI * freqs * t_c\n", + "# Ignoring the `t_c`` contribution" + ] + }, + { + "cell_type": "markdown", + "id": "49cef49d", + "metadata": {}, + "source": [ + "### Raw phase comparison\n", + "\n", + "Just to show that all three are \"the same\" on large scale" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "99ae1f98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n", + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/IPython/core/events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " func(*args, **kwargs)\n", + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", + "ax.plot(masked_freqs, lal_XAS_phase[freq_mask], label='LAL', c='grey')\n", + "ax.plot(masked_freqs, ripple_Psi[freq_mask], label='Ripple', ls='--')\n", + "ax.plot(masked_freqs, re_ripple_Psi[freq_mask], label='Re-Ripple', ls='-.')\n", + "ax.legend()\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'Phase, $\\Psi(f)$')\n", + "ax.set_xscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "55b20674", + "metadata": {}, + "source": [ + "### Phase difference\n", + "\n", + "How much do they differ?" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "5cdf0e29", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '← Off by a constant shift')" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", + "ax.plot(masked_freqs, ripple_Psi[freq_mask] - lal_XAS_phase[freq_mask], label='Ripple', ls='--')\n", + "ax.plot(masked_freqs, re_ripple_Psi[freq_mask] - lal_XAS_phase[freq_mask], label='Re-Ripple', ls='-.')\n", + "ax.legend()\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'Phase difference, $\\Psi(f) - \\Psi_{\\rm LAL}(f)$')\n", + "ax.set_xscale('log')\n", + "ax.set_title('← Off by a constant shift')" + ] + }, + { + "cell_type": "markdown", + "id": "bfe06857", + "metadata": {}, + "source": [ + "### Phase difference (II)\n", + "\n", + "Is this the difference we expected?\n", + "\n", + "$$\n", + "\\Delta\\Psi = 3 \\phi_c - \\pi\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "ffcc77aa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return math.isfinite(val)\n", + "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "text/plain": [ + "(1e-09, 2e-07)" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "correction_term = 3 * phi_c + np.pi\n", + "diff_ripple = ripple_Psi[freq_mask] - lal_XAS_phase[freq_mask] + correction_term\n", + "diff_re_ripple = re_ripple_Psi[freq_mask] - lal_XAS_phase[freq_mask] + correction_term\n", + "\n", + "scale = 0.9\n", + "fig, axes = plt.subplots(1, 2, figsize=(2 * 6.6 * scale, 4.5 * scale))\n", + "ax = axes[0]\n", + "ax.plot(masked_freqs, diff_ripple, label='Ripple', ls='--')\n", + "ax.plot(masked_freqs, diff_re_ripple, label='Re-Ripple', ls='-.')\n", + "ax.legend()\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'$\\Psi(f) - \\Psi_{\\rm LAL}(f) + \\Delta\\Psi$')\n", + "ax.set_title('Corrected difference')\n", + "ax.set_xscale('log')\n", + "\n", + "ax = axes[1]\n", + "ax.loglog(masked_freqs, np.abs(diff_ripple / lal_XAS_phase[freq_mask]), label='Ripple', ls='--')\n", + "ax.loglog(masked_freqs, np.abs(diff_re_ripple / lal_XAS_phase[freq_mask]), label='Re-Ripple', ls='-.')\n", + "ax.legend()\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'$\\vert (\\Psi - \\Psi_{\\rm LAL} + \\Delta\\Psi) / \\Psi_{\\rm LAL}\\vert$')\n", + "ax.set_title('Corrected Fractional difference')\n", + "ax.set_ylim(1e-9, 2e-7)\n", + "# ax.set_xscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "81e14470", + "metadata": {}, + "source": [ + "At this point, it is clear that the phase difference is real, and the source can be traced back to the code.\n", + "\n", + "However, the frequency-decaying phase difference is still unknown.\n", + "\n", + "My hunch is either:\n", + "* there are missing terms in the inpiral phase\n", + "* or, we are using different flags somewhere\n", + "\n", + "I think the fact that this difference decays with frequency should be quite a strong hint, as most terms grows with frequency. \n", + "One simply needs to identify the terms that decays with frequency." + ] + }, + { + "cell_type": "markdown", + "id": "7537b93a", + "metadata": {}, + "source": [ + "### Consequences\n", + "\n", + "Since the apparent $\\pi$ shift is simply being accounted for at different stages in `ripple` and `LAL`, it won't be much of an issue when computing hp and hc.\n", + "\n", + "The $3 \\phi_c$ however, will remain as an issue, and we shall demonstrate it below:" + ] + }, + { + "cell_type": "markdown", + "id": "2931743e", + "metadata": {}, + "source": [ + "#### First, we show it for one case" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "d52a5843", + "metadata": {}, + "outputs": [], + "source": [ + "lal_hpc = SimInspiralChooseFDWaveform(\n", + " mass_1 * MSUN_SI, mass_2 * MSUN_SI,\n", + " 0.0, 0.0, chi_1,\n", + " 0.0, 0.0, chi_2,\n", + " luminosity_distance * MPC_SI,\n", + " inclination, \n", + " phi_c, \n", + " 0.0, 0.0, 0.0,\n", + " delta_f, f_min, f_max,\n", + " reference_frequency,\n", + " None,\n", + " SimInspiralGetApproximantFromString(\"IMRPhenomXAS\")\n", + ")\n", + "lal_hp = lal_hpc[0].data.data[freq_mask]\n", + "lal_hc = lal_hpc[1].data.data[freq_mask]\n", + "\n", + "\n", + "ripple_params = jnp.array([\n", + " chirp_mass, symmetric_mass_ratio, \n", + " chi_1, chi_2, luminosity_distance, \n", + " t_c, phi_c, inclination\n", + "])\n", + "ripple_hp, ripple_hc = gen_IMRPhenomXAS_hphc(masked_freqs, ripple_params, reference_frequency)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "0051e081", + "metadata": {}, + "outputs": [], + "source": [ + "## Extract phases from hp\n", + "lal_hp_amp = np.abs(lal_hp)\n", + "lal_exp_psi = lal_hp / lal_hp_amp\n", + "lal_hp_phase = np.unwrap(np.angle(lal_exp_psi))\n", + "\n", + "ripple_hp_amp = np.abs(ripple_hp)\n", + "ripple_exp_psi = ripple_hp / ripple_hp_amp\n", + "ripple_hp_phase = np.unwrap(np.angle(ripple_exp_psi))" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "1061fe81", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(9, 4), constrained_layout=True)\n", + "ax = axes[0]\n", + "ax.plot(masked_freqs, lal_hp_phase, label='LAL')\n", + "ax.plot(masked_freqs, ripple_hp_phase, label='Ripple', ls='--')\n", + "ax.set_xscale('log')\n", + "ax.legend()\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'$\\Psi(f)\\,/\\,{\\rm rad}$')\n", + "ax.set_title(r'Phase extracted from $\\tilde h_{+}(f)$')\n", + "\n", + "ax = axes[1]\n", + "ax.plot(masked_freqs, lal_hp_phase - ripple_hp_phase - 3 * phi_c)\n", + "ax.set_xlabel(r'Frequency, $f\\,/\\,{\\rm Hz}$')\n", + "ax.set_ylabel(r'$\\Psi_{\\rm LAL}(f) - \\Psi_{\\rm Ripple}(f) - 3 \\phi_c$')\n", + "ax.set_title('Corrected phase difference')\n", + "ax.set_xscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "8f182073", + "metadata": {}, + "source": [ + "Note that with the correction term, we recover the same residue as before." + ] + }, + { + "cell_type": "markdown", + "id": "3e9404fb", + "metadata": {}, + "source": [ + "#### Generic trend\n", + "\n", + "Here, we consider a range of $\\phi_c$, and show that the phase difference indeed follows the expected $3\\,\\phi_c$ trend." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "42884f84", + "metadata": {}, + "outputs": [], + "source": [ + "phase_diffs = []\n", + "nn = 20\n", + "phis = np.arange(nn) * (TWOPI / (nn + 1))\n", + "\n", + "for phi_c in phis:\n", + " lal_hpc = SimInspiralChooseFDWaveform(\n", + " mass_1 * MSUN_SI, mass_2 * MSUN_SI,\n", + " 0.0, 0.0, chi_1,\n", + " 0.0, 0.0, chi_2,\n", + " luminosity_distance * MPC_SI,\n", + " inclination, \n", + " phi_c, \n", + " 0.0, 0.0, 0.0,\n", + " delta_f, f_min, f_max,\n", + " reference_frequency,\n", + " None,\n", + " SimInspiralGetApproximantFromString(\"IMRPhenomXAS\")\n", + " )\n", + " lal_hp = lal_hpc[0].data.data[freq_mask]\n", + " lal_hc = lal_hpc[1].data.data[freq_mask]\n", + "\n", + "\n", + " ripple_params = jnp.array([\n", + " chirp_mass, symmetric_mass_ratio, \n", + " chi_1, chi_2, luminosity_distance, \n", + " t_c, phi_c, inclination\n", + " ])\n", + " ripple_hp, ripple_hc = gen_IMRPhenomXAS_hphc(masked_freqs, ripple_params, reference_frequency)\n", + " ## Extract phases from hp\n", + " lal_hp_amp = np.abs(lal_hp)\n", + " lal_exp_psi = lal_hp / lal_hp_amp\n", + " lal_hp_phase = np.unwrap(np.angle(lal_exp_psi))\n", + "\n", + " ripple_hp_amp = np.abs(ripple_hp)\n", + " ripple_exp_psi = ripple_hp / ripple_hp_amp\n", + " ripple_hp_phase = np.unwrap(np.angle(ripple_exp_psi))\n", + "\n", + " phase_diffs.append(np.mean(ripple_hp_phase - lal_hp_phase))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9623f536", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$\\\\Delta\\\\Psi\\\\,/\\\\,{\\\\rm rad}$')" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 4), constrained_layout=True)\n", + "ax.scatter(phis, - np.unwrap(phase_diffs), marker='o', label=r'$\\Psi_{\\rm LAL} - \\Psi_{\\rm ripple}$')\n", + "ax.plot(phis, 3 * phis, c='grey', label=r'$3\\,\\phi_c$')\n", + "ax.legend()\n", + "ax.set_xlabel(r'$\\phi_c\\,/\\,{\\rm rad}$')\n", + "ax.set_ylabel(r'$\\Delta\\Psi\\,/\\,{\\rm rad}$')" + ] + }, + { + "cell_type": "markdown", + "id": "08ffb3e7", + "metadata": {}, + "source": [ + "This concludes the trend is indeed true and exists." + ] + }, + { + "cell_type": "markdown", + "id": "68525f31", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py311_lal_ripple_bare", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 726e8d2fa9fb5d6003501551d12e309e27a77e59 Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Fri, 31 Oct 2025 21:40:14 +0800 Subject: [PATCH 025/474] Catch and filter warnings --- test/test_XAS_phase.ipynb | 66 ++++++++++++++------------------------- 1 file changed, 23 insertions(+), 43 deletions(-) diff --git a/test/test_XAS_phase.ipynb b/test/test_XAS_phase.ipynb index 1d85e4c5..f0c22848 100644 --- a/test/test_XAS_phase.ipynb +++ b/test/test_XAS_phase.ipynb @@ -128,7 +128,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "/tmp/ipykernel_958096/3184374212.py:13: UserWarning: Wswiglal-redir-stdio:\n", "\n", "SWIGLAL standard output/error redirection is enabled in IPython.\n", "This may lead to performance penalties. To disable locally, use:\n", @@ -150,7 +150,7 @@ "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", "import lal\n", "\n", - " import lal\n" + " from lal import MSUN_SI, MTSUN_SI, PC_SI, CreateDict, TWOPI\n" ] } ], @@ -174,7 +174,12 @@ " SimInspiralChooseFDWaveform, \\\n", " SimInspiralGetApproximantFromString\n", "\n", - "MPC_SI = PC_SI * 1e6" + "MPC_SI = PC_SI * 1e6\n", + "\n", + "# Skip the complex to real warnings\n", + "import warnings\n", + "warnings.catch_warnings()\n", + "warnings.simplefilter(\"ignore\")" ] }, { @@ -200,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 3, "id": "9f71eb5b", "metadata": {}, "outputs": [], @@ -247,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 4, "id": "daac93f5", "metadata": {}, "outputs": [], @@ -274,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 5, "id": "87eea73d", "metadata": {}, "outputs": [], @@ -305,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 6, "id": "ce0a229a", "metadata": {}, "outputs": [], @@ -332,24 +337,10 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 7, "id": "99ae1f98", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n", - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/IPython/core/events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " func(*args, **kwargs)\n", - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, { "data": { "image/png": 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", @@ -384,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 8, "id": "5cdf0e29", "metadata": {}, "outputs": [ @@ -394,7 +385,7 @@ "Text(0.5, 1.0, '← Off by a constant shift')" ] }, - "execution_count": 143, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -436,27 +427,17 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 9, "id": "ffcc77aa", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/users/hin-wai.leong/.conda/envs/py311_lal_ripple_bare/lib/python3.11/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, { "data": { "text/plain": [ "(1e-09, 2e-07)" ] }, - "execution_count": 144, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -537,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 10, "id": "d52a5843", "metadata": {}, "outputs": [], @@ -569,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 11, "id": "0051e081", "metadata": {}, "outputs": [], @@ -586,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 12, "id": "1061fe81", "metadata": {}, "outputs": [ @@ -640,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 13, "id": "42884f84", "metadata": {}, "outputs": [], @@ -666,7 +647,6 @@ " lal_hp = lal_hpc[0].data.data[freq_mask]\n", " lal_hc = lal_hpc[1].data.data[freq_mask]\n", "\n", - "\n", " ripple_params = jnp.array([\n", " chirp_mass, symmetric_mass_ratio, \n", " chi_1, chi_2, luminosity_distance, \n", @@ -687,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "9623f536", "metadata": {}, "outputs": [ @@ -697,7 +677,7 @@ "Text(0, 0.5, '$\\\\Delta\\\\Psi\\\\,/\\\\,{\\\\rm rad}$')" ] }, - "execution_count": 161, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, From 64ae71434bccd91f75ab63c1766236c5235bd91f Mon Sep 17 00:00:00 2001 From: "Samson Leong (wkstn)" <55839002+SSL32081@users.noreply.github.com> Date: Fri, 31 Oct 2025 21:58:41 +0800 Subject: [PATCH 026/474] Fix phase difference in PhenomXAS --- src/ripplegw/waveforms/IMRPhenomXAS.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index b9b8c959..9edf1688 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -1351,12 +1351,13 @@ def _gen_IMRPhenomXAS( jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, theta_intrinsic, phase_coeffs) / M_s ) linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + # The addition π shift comes from Y22 phifRef = ( -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + PI / 4.0 + PI ) - ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib A = Amp(f, theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) From a8893a849af059417861d685094be330272d60e4 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Fri, 31 Oct 2025 15:59:12 +0100 Subject: [PATCH 027/474] Added 3\phi_c patch --- src/ripplegw/waveforms/IMRPhenomXAS.py | 57 ++++++++++++++++--- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 15 ++--- 2 files changed, 57 insertions(+), 15 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index 44f8220d..777c0947 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -1324,7 +1324,6 @@ def _gen_IMRPhenomXAS( phase_coeffs: Array, amp_coeffs: Array, f_ref: float, - get_phase: bool = False ): m1, m2, chi1, chi2 = theta_intrinsic m1_s = m1 * gt @@ -1352,24 +1351,21 @@ def _gen_IMRPhenomXAS( jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, theta_intrinsic, phase_coeffs) / M_s ) linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + # The addition π shift comes from Y22 phifRef = ( -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + PI / 4.0 + PI ) - ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib - # DEBUG FEATURE - if get_phase: - return Psi, lina, linb, fM_s, phifRef, psi4tostrain - A = Amp(f, theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) h0 = A * jnp.exp(1j * Psi) return h0 -def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float, get_phase: bool = False): +def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float): """ Generate PhenomXAS frequency domain waveform following 2001.11412. Note that this waveform also assumes that object one is the more massive. @@ -1395,7 +1391,7 @@ def gen_IMRPhenomXAS(f: Array, params: Array, f_ref: float, get_phase: bool = Fa amp_coeffs = IMRPhenomX_utils.PhenomX_amp_coeff_table h0 = _gen_IMRPhenomXAS( - f, theta_intrinsic, theta_extrinsic, phase_coeffs, amp_coeffs, f_ref, get_phase=get_phase + f, theta_intrinsic, theta_extrinsic, phase_coeffs, amp_coeffs, f_ref ) return h0 @@ -1428,3 +1424,48 @@ def gen_IMRPhenomXAS_hphc(f: Array, params: Array, f_ref: float): hc = -1j * h0 * jnp.cos(iota) return hp, hc + + +# Debug function +def get_IMRPhenomXAS_Phase( + f: Array, + theta_intrinsic: Array, + theta_extrinsic: Array, + phase_coeffs: Array, + f_ref: float, +): + m1, m2, chi1, chi2 = theta_intrinsic + m1_s = m1 * gt + m2_s = m2 * gt + + M_s = m1_s + m2_s + eta = m1_s * m2_s / (M_s**2.0) + delta = jnp.sqrt(1.0 - 4.0 * eta) + mm1 = 0.5 * (1.0 + delta) + mm2 = 0.5 * (1.0 - delta) + + StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) + chia = chi1 - chi2 + + fM_s = f * M_s + fMs_RD, fMs_damp, _, _ = IMRPhenomX_utils.get_cutoff_fMs(m1, m2, chi1, chi2) + Psi = Phase(f, theta_intrinsic, phase_coeffs) + + # Generate the linear in f and constant contribution to the phase in order + # to roll the waveform such that the peak is at the input tc and phic + lina, linb, psi4tostrain = IMRPhenomX_utils.calc_phaseatpeak( + eta, StotR, chia, delta + ) + dphi22Ref = ( + jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, theta_intrinsic, phase_coeffs) / M_s + ) + linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + phifRef = ( + -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + + PI / 4.0 + + PI + ) + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib + + return Psi diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index 0bd55f5d..a6cc78a2 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -54,11 +54,11 @@ def _gen_IMRPhenomXAS_NRTidalv3( # Decide whether to include the Planck taper or not if no_taper: + P_P = jnp.ones_like(f) A_P = jnp.ones_like(f) - AA_P = jnp.ones_like(f) else: - A_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) - AA_P = get_planck_taper(f, f_merger) + P_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) + A_P = get_planck_taper(f, f_merger) # Get tidal phase and spin corrections for BNS PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) @@ -73,14 +73,14 @@ def _gen_IMRPhenomXAS_NRTidalv3( valid = (f >= fHzmrgcheck) & increasing - psi_T = NRTidalv3_phase * (1 - A_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * A_P + psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) if get_phase: # purely for debugging purposes return bbh_psi + psi_T + psi_SS # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) - h0 = AA_P * (bbh_amp + A_T) * jnp.exp(1.0j * (bbh_psi + psi_T + psi_SS)) + h0 = A_P * (bbh_amp + A_T) * jnp.exp(1.0j * (bbh_psi + psi_T + psi_SS)) return h0 @@ -156,12 +156,13 @@ def gen_IMRPhenomXAS_NRTidalv3( jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, bbh_theta_intrinsic, phase_coeffs) / M_s ) linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + # The addition π shift comes from Y22 phifRef = ( - -(Phase(f_ref, bbh_theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + PI / 4.0 + PI ) - ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib A = Amp(f, bbh_theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) From 2f0bad28b66217fe94704ff69cf181f88bc21774 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Wed, 5 Nov 2025 09:28:12 +0100 Subject: [PATCH 028/474] Implementing tidal phase alignment --- src/ripplegw/waveforms/IMRPhenomXAS.py | 3 +- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 138 +++-- test/NRTidalv3_testing.ipynb | 577 ++++++++++-------- 3 files changed, 431 insertions(+), 287 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index 777c0947..d6813f44 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -1460,12 +1460,13 @@ def get_IMRPhenomXAS_Phase( jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, theta_intrinsic, phase_coeffs) / M_s ) linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + # The additional π shift comes from Y22 phifRef = ( -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) + PI / 4.0 + PI ) - ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] - theta_extrinsic[2] + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib return Psi diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index a6cc78a2..34e66c42 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -14,9 +14,33 @@ import lal +def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, f_final: float, no_taper: bool): + # Decide whether to include the Planck taper or not + if no_taper: + P_P = jnp.ones_like(f) + else: + P_P = general_planck_taper(f, 1.15*f_final, 1.35*f_final) + + m1, m2, _, _, lambda1, lambda2 = theta_intrinsic + M_s = (m1 + m2) * gt + Xa = m1 / (m1 + m2) + x = PI * f * M_s + x_23 = x**(2.0/3.0) + + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) + + psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P + psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) + + return psi_T + psi_SS + + # This could be a general utils function def _gen_IMRPhenomXAS_NRTidalv3( f: Array, + f_ref: float, theta_intrinsic: Array, theta_extrinsic: Array, bbh_amp: Array, @@ -44,6 +68,7 @@ def _gen_IMRPhenomXAS_NRTidalv3( Xa = m1 / (m1 + m2) x = PI * f * M_s x_23 = x**(2.0/3.0) + f_Ms = f * M_s # Compute kappa kappa = get_kappa(theta=theta_intrinsic) @@ -52,35 +77,57 @@ def _gen_IMRPhenomXAS_NRTidalv3( A_T = get_tidal_amplitude(x_23, theta_intrinsic, kappa, distance=theta_extrinsic[0]) f_merger = _get_merger_frequency(theta_intrinsic) - # Decide whether to include the Planck taper or not + # Tidal phase offset # + f_final = f[-1] + if f_merger < f_final: + f_final = f_merger + + bbh_phase_coeffs = IMRPhenomX_utils.PhenomX_phase_coeff_table + # Note: the π shift from Y22 has been moved to the calculation of h0 + phifRef = ( + Phase(f_ref, theta_intrinsic[:4], bbh_phase_coeffs) + - PI / 4.0 + ) # This is part of the BBH phase alignment + phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, f_final, no_taper) # This is part of the tidal correction to the phase alignment + + dphi_merger = -jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs)\ + + jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, f_final, no_taper) + ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] + + phase_shift = -(phifRef - phiTfRef + dphi_merger*(f_ref*M_s)) + dphi_merger*f_Ms + ext_phase_contrib + + # Get tidal phase and spin corrections for BNS + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) + + # # TODO: Check for local minimum -> this doesn't seem to work correctly at the moment + # fHzmrgcheck = 0.9 * f_merger + # increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) + # valid = (f >= fHzmrgcheck) & increasing + # if jnp.any(valid): # if local minimum found: set NRTidalv3 phase to this vale afterwards + # idx = jnp.argmax(valid) + # tidal_min_value = NRTidalv3_phase[idx] + # mask = (jnp.arange(f.size) >= idx) + # NRTidalv3_phase = jnp.where(mask, tidal_min_value, NRTidalv3_phase) + + + # Redefine planck taper as LAL uses the merger frequency for this computation, not f_final (which can be f_merger, but isn't guaranteed) if no_taper: P_P = jnp.ones_like(f) A_P = jnp.ones_like(f) else: P_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) A_P = get_planck_taper(f, f_merger) - - # Get tidal phase and spin corrections for BNS - PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) - NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) - NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) - - # TODO: add tidal phase offset - - # Check for local minimum TODO - fHzmrgcheck = 0.9 * f_merger - increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) - valid = (f >= fHzmrgcheck) & increasing - - + psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) if get_phase: # purely for debugging purposes - return bbh_psi + psi_T + psi_SS + return bbh_psi, phase_shift, phifRef, phiTfRef, dphi_merger, psi_T, psi_SS # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) - h0 = A_P * (bbh_amp + A_T) * jnp.exp(1.0j * (bbh_psi + psi_T + psi_SS)) + h0 = A_P * (bbh_amp + A_T) * jnp.exp(1.0j * ((bbh_psi + phase_shift + PI) - (psi_T + psi_SS))) # The additional π shift comes from Y22 return h0 @@ -131,39 +178,42 @@ def gen_IMRPhenomXAS_NRTidalv3( # Generate the BBH part: bbh_theta_intrinsic = jnp.array([m1, m2, chi1, chi2]) - m1_s = m1 * gt - m2_s = m2 * gt + # m1_s = m1 * gt + # m2_s = m2 * gt - M_s = m1_s + m2_s - eta = m1_s * m2_s / (M_s**2.0) - delta = jnp.sqrt(1.0 - 4.0 * eta) - mm1 = 0.5 * (1.0 + delta) - mm2 = 0.5 * (1.0 - delta) + # M_s = m1_s + m2_s + # eta = m1_s * m2_s / (M_s**2.0) + # delta = jnp.sqrt(1.0 - 4.0 * eta) + # mm1 = 0.5 * (1.0 + delta) + # mm2 = 0.5 * (1.0 - delta) - StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) - chia = chi1 - chi2 + # StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) + # chia = chi1 - chi2 - fM_s = f * M_s - fMs_RD, fMs_damp, _, _ = IMRPhenomX_utils.get_cutoff_fMs(m1, m2, chi1, chi2) + # fM_s = f * M_s + # fMs_RD, fMs_damp, _, _ = IMRPhenomX_utils.get_cutoff_fMs(m1, m2, chi1, chi2) Psi = Phase(f, bbh_theta_intrinsic, phase_coeffs) + # linb is cancelled by itself in 737-738 of LALSimIMRPhenomX.c and replaced by its tidal equivalent + # For ease of computation and structure, the phase alignment terms are added in _gen_IMRPhenomXAS_NRTidalv3 (even the non-tidal ones as the tidal merger frequency is different from the BBH merger frequency) + # Generate the linear in f and constant contribution to the phase in order # to roll the waveform such that the peak is at the input tc and phic - lina, linb, psi4tostrain = IMRPhenomX_utils.calc_phaseatpeak( - eta, StotR, chia, delta - ) - dphi22Ref = ( - jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, bbh_theta_intrinsic, phase_coeffs) / M_s - ) - linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) - # The addition π shift comes from Y22 - phifRef = ( - -(Phase(f_ref, theta_intrinsic, phase_coeffs) + linb * (f_ref * M_s) + lina) - + PI / 4.0 - + PI - ) - ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] - Psi = Psi + (linb * fM_s) + lina + phifRef - 2 * PI + ext_phase_contrib + # lina, linb, psi4tostrain = IMRPhenomX_utils.calc_phaseatpeak( + # eta, StotR, chia, delta + # ) + # dphi22Ref = ( + # jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, bbh_theta_intrinsic, phase_coeffs) / M_s + # ) + # linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) + # The additional π shift comes from Y22 + # phifRef = ( + # -(Phase(f_ref, theta_intrinsic[:4], phase_coeffs)) + # + PI / 4.0 + # + PI + # ) + # ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] + # Psi = Psi + phifRef - 2 * PI + ext_phase_contrib A = Amp(f, bbh_theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) @@ -172,7 +222,7 @@ def gen_IMRPhenomXAS_NRTidalv3( # Use BBH waveform and add tidal corrections return _gen_IMRPhenomXAS_NRTidalv3( - f, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper, get_phase=get_phase + f, f_ref, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper, get_phase=get_phase ) diff --git a/test/NRTidalv3_testing.ipynb b/test/NRTidalv3_testing.ipynb index b1d497e9..8e232edc 100644 --- a/test/NRTidalv3_testing.ipynb +++ b/test/NRTidalv3_testing.ipynb @@ -2,10 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": 66, + "execution_count": 1, "id": "22705cfa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", + "\n", + "SWIGLAL standard output/error redirection is enabled in IPython.\n", + "This may lead to performance penalties. To disable locally, use:\n", + "\n", + "with lal.no_swig_redirect_standard_output_error():\n", + " ...\n", + "\n", + "To disable globally, use:\n", + "\n", + "lal.swig_redirect_standard_output_error(False)\n", + "\n", + "Note however that this will likely lead to error messages from\n", + "LAL functions being either misdirected or lost when called from\n", + "Jupyter notebooks.\n", + "\n", + "To suppress this warning, use:\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", + "import lal\n", + "\n", + " import lal\n" + ] + } + ], "source": [ "import jax\n", "import jax.numpy as jnp\n", @@ -22,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 2, "id": "7bd3da0b", "metadata": {}, "outputs": [], @@ -43,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 3, "id": "3f33f2db", "metadata": {}, "outputs": [], @@ -84,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 4, "id": "a330c909", "metadata": {}, "outputs": [], @@ -95,9 +125,25 @@ "from ripplegw.waveforms.IMRPhenom_tidal_utils import get_kappa" ] }, + { + "cell_type": "markdown", + "id": "c66750f6", + "metadata": {}, + "source": [ + "## Some inital sanity checks:" + ] + }, + { + "cell_type": "markdown", + "id": "be23d51e", + "metadata": {}, + "source": [ + "### Merger frequency" + ] + }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 46, "id": "a65d3d67", "metadata": {}, "outputs": [ @@ -105,21 +151,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "1595.8424\n" + "1595.8424\n", + "1595.8423419624833\n" ] } ], "source": [ "theta_intrinsic = jnp.array([m1, m2, chi1, chi2, lambda1, lambda2])\n", - "M_s = m1 + m2\n", - "Xa = m1/M_s\n", + "M = m1 + m2\n", + "Xa = m1/M\n", "f_merger_ripple = _get_merger_frequency(theta_intrinsic)\n", - "print(f_merger_ripple)" + "print(f_merger_ripple)\n", + "f_merger_LAL = lalsim.SimNRTunedTidesMergerFrequency_v3(M, lambda1, lambda2, q, chi1, chi2)\n", + "print(f_merger_LAL)" + ] + }, + { + "cell_type": "markdown", + "id": "36d14995", + "metadata": {}, + "source": [ + "### Tidal phase components" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 70, "id": "c1dbdc6b", "metadata": {}, "outputs": [], @@ -127,20 +184,29 @@ "PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic)\n", "NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs)\n", "\n", - "M_omega = (PI * M_s *gt * fs_ripple)\n", - "A_P = general_planck_taper(fs, 1.15*f_merger_ripple, 1.35*f_merger_ripple)\n", + "M_s = M * gt\n", + "M_omega = (PI * M_s * fs_ripple)\n", + "P_P = general_planck_taper(fs, 1.15*f_merger_ripple, 1.35*f_merger_ripple)\n", "\n", "kappa = get_kappa(jnp.array([m1,m2,chi1,chi2,lambda1,lambda2]))\n", "tidal_amp = get_tidal_amplitude(M_omega**(2/3), theta_intrinsic, kappa, distance=dist_mpc)\n", "\n", "tidal_phase = get_tidal_phase(M_omega, NRTidalv3_coeffs, PN_coeffs)\n", "tidal_phasePN = get_tidal_phase_PN(M_omega, Xa, lambda1, lambda2, PN_coeffs)\n", - "tidal_phase_corr = tidal_phase * (1 - A_P) + tidal_phasePN * A_P" + "tidal_phase_corr = tidal_phase * (1 - P_P) + tidal_phasePN * P_P" + ] + }, + { + "cell_type": "markdown", + "id": "aca185ff", + "metadata": {}, + "source": [ + "The following cells are a test for the local minimum of the tidal phase. It doesn't work yet, so don't pay too much attention to it" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 51, "id": "610906ab", "metadata": {}, "outputs": [ @@ -175,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 52, "id": "f842e550", "metadata": {}, "outputs": [ @@ -202,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 53, "id": "9d8657b1", "metadata": {}, "outputs": [ @@ -218,7 +284,7 @@ } ], "source": [ - "tidal_phase_corr_withMIN = phi_tidal * (1 - A_P) + tidal_phasePN * A_P\n", + "tidal_phase_corr_withMIN = phi_tidal * (1 - P_P) + tidal_phasePN * P_P\n", "plt.plot(fs, phi_tidal,label='Phase corr')\n", "plt.plot(fs, tidal_phasePN,label='PN corr')\n", "plt.plot(fs, tidal_phase_corr_withMIN, ls='--',label='Full corr')\n", @@ -230,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 54, "id": "d943c569", "metadata": {}, "outputs": [ @@ -240,7 +306,7 @@ "0" ] }, - "execution_count": 75, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -290,15 +356,15 @@ }, { "cell_type": "code", - "execution_count": 76, - "id": "fcfeb8b7", + "execution_count": 55, + "id": "c3339856", "metadata": {}, "outputs": [ { "data": { - "image/png": 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fadWqVerbt6+1vKLH3pH+fOkcsJe9x9eV51R5Pv30Uz300EPau3evWrZsKUkaPHiwCgsL9cwzz+gf//iHWz4nHdk+b90Xrvq8hXcg6QdcZOzYsZo3b57GjRun7t27KyQkRCaTScOGDVNhYaHL+inrw7esKwfFE3t7YxwyZIj1yvSKFSs0bdo0vfzyy/ryyy/Vv39/a917771XsbGxpfZbfBSCpzgT54W/XJ3dVnuOR3lcsY8bNWqkc+fOKTMzU3Xr1rWWb9++Xc2bNy8ximPbtm3q2LFjiSsO27Zts+mrefPmmjx5shYuXGjXtpSmIjGcO3dOrVu31rp169SyZUstXrxYAwYM0MGDB0tcoTpz5oxq165d5h9N+fn5On36tF0xN2nSpNTjWtZ+dpc777xTDz/8sPbu3at27dqVWL906VKdPXtW99xzj9tjcUR5cdsb88WOZ5GLHZOiL9xef/113Xbbbbr33ntt1rviHL9YP750Hjdv3lxHjhwp8Z5jx45JUqkjwsriaFsVPeY//vijjh07phdffNGm/u23367atWtrwYIFNkl/RY+9I/350jlgL3uPryvPqfK8/fbb6ty5szXJLXL77bdr/vz5+u2339zyZYkj2+et+8Le/3vwDST9QBmaNGmiWrVqad++fSXW7dmzp0TZ559/rtjYWL322mvWstzcXLtm2S7Pvn37bK4+79+/X4WFhU5NzuVIjM2bN9fo0aM1evRoHT9+XNdcc42mTJmi/v37q0mTJqpbt64sFovDvywd3a8VUZE4XdlGaW3Wq1dPO3bscGu/RVdZk5OTbZL27du3KyIiokT9bdu26dZbb7UpKygo0J49e2yGXA8cOFCSKjQqoyIxBAcHa+LEidb1w4YNU3x8vPbs2aMuXbrYvDc5OVnt27cvM47169erd+/edsWcnJxc6v+7svazuxQN9SxrBNGCBQtUp06dEqMoPK28uO2N+WLHs8jFjknRlw7169cv9ckhrjjHL9aPL53HkZGR+umnn2xuF5OkjRs3Wtfby9G2KnrMFyxYoOrVq2vQoEE29YODg3XrrbdqyZIlmj17tjWxqeixd6Q/XzoH7GXv8XXlOVWe1NRU60i74goKCiSd/+LFHRzZPm/dF/b+34Nv4JF9QBkCAwPVr18/LV26VH/++ae1fNeuXfr+++9LrX/hVdy33npLFoulQnGYzeYSbUpS//79HW7LnhgtFkuJP8qbNm2qFi1aWIefBQYG6u9//7u++OKLUhPXEydOlBuDI/u1IioSpyvbuFBAQIAGDhyor7/+Wps3by6x3jAMl/RbNES0eB8Wi0VJSUklEu6TJ0/q2LFjJcp37dqlgoIClyazro5h3759On36tNq2bVti3datW/W3v/2tzFiK7oO1ZynrPtjS9rOjzp49q927d9vM3VDa7RsFBQX68MMPVatWLYWHh5dYf+LECa1cuVKDBg1y+z2YpcUsOR63IzFf7HgWudgxmTt3rqTzV7hcdRWtov1483l85513ymKx2NxqlJeXp3nz5ik6OtqhmcUdbasixzwnJ0dffvml+vbtW2qyM3ToUGVmZup///uf3fGXp6L9efM5YC97j68rz6niLvxcuvLKK/Xbb79p7969NvU++eQTBQQEuO2LWke2zx37orTPZ0f3hb3/9+AbuNIPlOO5557T8uXL1aNHD40ePVrnzp3TW2+9pQ4dOlgnIity22236aOPPlJISIjCw8O1YcMGrVy5ssL3iiUnJ+v222/XzTffrA0bNujjjz/W3XffXepV0ouxJ8bMzEy1bNlSd955pyIiIlSnTh2tXLlSCQkJNiME/vvf/+qnn35SdHS0HnzwQYWHh+v06dPaunWrVq5cWe4wQ0f2a0VVJE5XtnGhl156SStWrFDPnj310EMPqX379jp27Jg+++wzrVu3TvXr169wv5dddpk6duyolStXauTIkZLO/1GZm5tb4vwpehTeheVFx8PRP4xMJpN69uyp1atXl1jnyhhycnJ07733asKECQoJCbFZt2XLFp0+fVp33HFHmXG64j7Y0vZzkZkzZyotLU1Hjx6VJH399df666+/JJ2/3aYo5k2bNql3796aNGmS9R7ihx9+WBkZGbrhhht0ySWXKCUlRQsWLNDu3bv12muvlTrZ1qJFi3Tu3Llyh8mXd2wcibu0mJ2J256YJfuOZ5HyjsmBAwf07LPPSpJ279590bbKcrH96Eg/3n4eR0dH66677tKECRN0/PhxtW3bVh988IEOHjyo9957z6buxfaLI21V9Jj/73//U2ZmZpmPirz11ltVt25dLViw4KKPwbRnGyvSn7efA/Z+ltl7fB05Dxzp/8LPpaeeekrLli1Tjx49NGbMGDVq1EjffPONli1bplGjRpX4Mu5i56+r94OjdSvyO8WRfeHI/z34iEp9VgDgBRx5ZJ9hGMbPP/9sdOnSxahRo4Zx2WWXGbNnz7Y+lq24M2fOGCNGjDAaN25s1KlTx+jXr5+xe/du49JLLzViY2PL7aM0RX0kJSUZd955p1G3bl2jQYMGxpgxY4ycnJwS9U6cOHHRbbEnxry8POOpp54yIiIijLp16xrBwcFGREREqY/RSk1NNeLi4oywsDCjevXqRrNmzYybbrrJmDNnTrnb5sh+regj++yNs6z96Oi2OnI8Dh06ZNx///1GkyZNjKCgIOOyyy4z4uLijLy8PIf7Lcvrr79u1KlTx/p4v8WLFxuSjB07dpSoJ8lIS0uzKX/66aeNevXqWR8jWNzDDz9sTJo0qUR5ZmamIckYNmxYqTG5Kob8/Hzj1ltvNe6+++5S43vmmWeMVq1albrO1S7cz0UuvfRSux6bVXT+Ft+fn3zyiRETE2OEhoYa1apVMxo0aGDExMQYX331VZlxXHvttUbTpk2Nc+fOlbr+YsfGkbhLi9mZuC8WcxFHj2dpx6SwsNDo2bOn0aBBA2PEiBFGnTp1ym3P2XPckX584Tw2DMPIyckxnnzySaNZs2ZGUFCQERUVZSxfvtymjr3nlz1tGUbFj/mAAQOMoKAgm8d/Xuiee+4xqlevXuLRqM4ce2f784VzwN7PMsOw//jaW8+R/kv7XNq4caPRv39/o1mzZkb16tWNK6+80pgyZYpRUFBg04c956879oMjdSuyHxzZF5V5zqFykPQDXupiSShgj7S0NKNhw4bGu+++6/K2y/qj+NtvvzVMJpOxfft2l/dZxGKxGEOHDjVuu+22En+sGIZh5ObmGs2aNTOmT5/uthiKc+d+dqXKODbu4MzxLO2YzJw505BkfPjhh8Ynn3xiSDIOHDhQZhvOnuP29uNv57Erzy9XHXNnVdbnm7+dA77MVz8fXa2yzzlUDu7pBwA/FhISoqefflrTpk1z2VMkzp07p9zcXFksFpufi/z0008aNmyYOnXq5JL+SvPwww9bb4eoVq3knWrz5s1T9erV9cgjj7gthuLcsZ/doTKOjTs4czwvPCYHDx7U+PHjNWDAAN13333WfbB169YS763IOe5IP/52Hrvy/HLFMXdGZX+++ds54Mt89fPR1Sr7nEMl8fS3DgBKx5V+eKuic7P4Mm/evErr/+DBg4Yko2bNmkZwcLB1WbNmTaXFAN9SWFho3HjjjUaDBg2Mo0ePGoZhGAUFBUadOnWMK6+80njnnXeMrKwsa31nz3FH+uE89k6V+fnGOQCgspgMw86HRgOoVJMnT9Zzzz2nEydOqHHjxp4OBwB81jvvvKNHHnlEH374oe677z5r+fz58/Wf//xHJ06cUGZmpqpXr+4T/QAA4AiSfgAAAAAA/FSVv6f/8OHD6tWrl8LDw3X11Vfrs88+83RIAAAAAAC4RJW/0n/s2DGlpqYqMjJSKSkp6tKli/bu3avg4GBPhwYAAAAAQIWUnCa0imnevLmaN28uSWrWrJkaN26s06dP2530FxYW6ujRo6pbt65MJpM7QwUAAAAAQIZhKDMzUy1atFBAQPkD+L0+6V+zZo2mTZumLVu26NixY1qyZIkGDhxoU8dsNmvatGlKSUlRRESE3nrrLXXr1s3hvrZs2SKLxaKwsDC733P06FGH6gMAAAAA4AqHDx9Wy5Yty63j9Ul/dna2IiIiNHLkSA0ePLjE+kWLFik+Pl6zZ89WdHS0pk+frn79+mnPnj1q2rSpJCkyMlLnzp0r8d4VK1aoRYsWkqTTp0/r/vvv19y5cx2Kr27dupLO7+x69eo5unkAAAAAAC9VWFiow4cPS5LCwsIuelW9smIJCQnRpZdeas1Hy+NT9/SbTKYSV/qjo6MVFRWlmTNnSjq/I8LCwjR27FiNHz/ernbz8vLUp08fPfjggzaP2Cmrbl5envV1RkaGwsLClJ6eTtIPAAAAAH4kOztbderUkSRlZWV5dO634rEcPXpULVq0sCsP9enZ+/Pz87VlyxbFxMRYywICAhQTE6MNGzbY1YZhGBo+fLhuvPHGiyb8kjR16lSFhIRYF4b2AwAAAAC8lU8n/SdPnpTFYlFoaKhNeWhoqFJSUuxq45dfftGiRYu0dOlSRUZGKjIyUr///nuZ9SdMmKD09HTrUjS8AgAAAAAAb+P19/S72/XXX6/CwkK76wcFBSkoKEhms1lms1kWi8WN0QEAAAAA4DyfvtLfuHFjBQYGKjU11aY8NTVVzZo1c2vfcXFxSkpKUkJCglv7AQAAAADAWT6d9NeoUUNdunTRqlWrrGWFhYVatWqVunfv7sHIAAAAAADwPK8f3p+VlaX9+/dbXycnJysxMVENGzZUq1atFB8fr9jYWHXt2lXdunXT9OnTlZ2drREjRrg1Lob3AwAAAAC8ndcn/Zs3b1bv3r2tr+Pj4yVJsbGxmj9/voYOHaoTJ05o4sSJSklJUWRkpJYvX15icj9Xi4uLU1xcnDIyMhQSEuLWvgAAAAAAla9atWoaPXq09WdfjMVkGIbhrqCqgqKk357nIwIAAAAAUFGO5KE+fU+/J5nNZoWHhysqKsrToQAAAAAAUCqu9FcQV/oBAAAAwD8ZhqGTJ09KOv/0OJPJ5BWx1KhRQ/Xr17crD/X6e/oBAAAAAPCEs2fPqmnTppLOTzIfHBzsFbEcPXrU7vcxvN9JDO8HAAAAAHg7kn4nxcXFKSkpSQkJCZ4OBQAAAACAUpH0AwAAAADgp0j6AQAAAADwUyT9TuKefgAAAACAtyPpdxL39AMAAAAAvB2P7AMAAAAAoBTVqlVTbGys9WdfjMVkGIbhrqCqgoyMDIWEhCg9PV316tXzdDgAAAAAAD/nSB7K8H4AAAAAAPwUSb+TmMgPAAAAAPybYRjKzs5Wdna2PD1I3tlYGN5fQQzvBwAAAAD/lJ2drTp16kiSsrKyFBwc7BWxHD16VC1atLArD2UiPwAAAAAAvNiff/6ppKQk6+u8vDy730vSDwAAAACAF8rPz1dQUFCJ8jZt2tjdBvf0AwAAAADgRTZu3CiTyVRqwu8orvQDAAAAAOAFtm3bpsjISJe2SdIPAAAAAICHmUwmt7TL8H4n8cg+AAAAAEBFff75525L+CUe2VdhPLIPAAAAAPxTbm6u7rvvPknSRx99pJo1a7qs7ZSUFDVv3rxCbdiTh5L0VxBJPwAAAADAXkU5pCvYk4cyvB8AAAAAgErQu3dvlyX89iLpBwAAAADATfLz8zVq1CiZTCatXr260vsn6QcAAAAAoBTZ2dkymUwymUzKzs52+P0zZsxQUFCQ3nvvPTdEZx8e2QcAAAAAgAsVFhYqMDDQ02FI4ko/AAAAAAAuYRiG1q1b5zUJv8SVfgAAAAAAXCIgwPuuq3tfRD7CbDYrPDxcUVFRng4FAAAAAIBSmQzDMDwdhC8resaiPc9HBAAAAAD4juzsbNWpU0eSlJWVpeDg4HLrm0ymygjLyp48lCv9AAAAAAD4Ke7pBwAAAACgFIGBgbrlllusP/sikn4AAAAAAEpRs2ZNffvtt54Oo0IY3g8AAAAAgJ8i6QcAAAAAwE+R9AMAAAAAUIrs7GwFBwcrODhY2dnZng7HKdzTDwAAAABAGc6ePevpECqEK/0AAAAAAPipKp/0p6WlqWvXroqMjFTHjh01d+5cT4cEAAAAAIBLVPnh/XXr1tWaNWtUu3ZtZWdnq2PHjho8eLAaNWrk6dAAAAAAAKiQKn+lPzAwULVr15Yk5eXlyTAMGYbh4agAAAAAAKg4r0/616xZowEDBqhFixYymUxaunRpiTpms1mtW7dWzZo1FR0drU2bNjnUR1pamiIiItSyZUs99dRTaty4sYuiBwAAAADAc7w+6c/OzlZERITMZnOp6xctWqT4+HhNmjRJW7duVUREhPr166fjx49b6xTdr3/hcvToUUlS/fr1tW3bNiUnJ2vhwoVKTU2tlG0DAAAAAHivgIAA9ezZUz179lRAQNnpc0ZGhkwmUyVGZj+T4UNj2U0mk5YsWaKBAwday6KjoxUVFaWZM2dKkgoLCxUWFqaxY8dq/PjxDvcxevRo3XjjjbrzzjtLXZ+Xl6e8vDzr64yMDIWFhSk9PV316tVzuD8AAAAAgO/Zt2+fvv/+e40dO9ZjMdiTh3r9lf7y5Ofna8uWLYqJibGWBQQEKCYmRhs2bLCrjdTUVGVmZko6v8PWrFmjdu3alVl/6tSpCgkJsS5hYWEV2wgAAAAAgE9ZuXKlrrzySo8m/Pby6aT/5MmTslgsCg0NtSkPDQ1VSkqKXW0cOnRIPXr0UEREhHr06KGxY8eqU6dOZdafMGGC0tPTrcvhw4crtA0AAAAAAN/x1VdfqU+fPp4Ow25V/pF93bp1U2Jiot31g4KCFBQUJLPZLLPZLIvF4r7gAAAAAAAek52drdatW0uSDhw4oJCQEM8G5ASfvtLfuHFjBQYGlph4LzU1Vc2aNXNr33FxcUpKSlJCQoJb+wEAAAAAeM7Jkyd18uRJn0z4JR9P+mvUqKEuXbpo1apV1rLCwkKtWrVK3bt392BkAAAAAABfl5ub6+kQKszrh/dnZWVp//791tfJyclKTExUw4YN1apVK8XHxys2NlZdu3ZVt27dNH36dGVnZ2vEiBFujYvh/QAAAADgnywWizZs2KAePXp4OpQK8/pH9q1evVq9e/cuUR4bG6v58+dLkmbOnKlp06YpJSVFkZGRmjFjhqKjoyslvoyMDIWEhPDIPgAAAADwA0U5ni+wJw/1+qTf25H0AwAAAIB/CAsL019//eXpMOxmTx7q0/f0e5LZbFZ4eLiioqI8HQoAAAAAoALS0tJkMpl8KuG3F1f6K4gr/QAAAADge/Lz8/Xf//5XL7/8ss6ePevpcJxiTx7q9RP5AQAAAADgauPHj9cbb7zh6TDcjuH9TmJ4PwAAAAD4ptTU1CqR8EsM768whvcDAAAAgG8wDENRUVHasmWLp0NxCSbyAwAAAABA0rx58xQQEOA3Cb+9SPoBAAAAAH4rJSVFt956q0aOHOnpUDyCifycZDabZTabZbFYPB0KAAAAAOACx48fV2hoqKfD8Dju6a8g7ukHAAAAAO9jMpk8HYLbcU8/AAAAAKDKMAxDu3btqhIJv71I+gEAAAAAfmHs2LEKDw/3dBhehaQfAAAAAODzXn31VZnNZk+H4XWYyM9JTOQHAAAAAJ734YcfKjY21tNheC0m8qsgJvIDAAAAAM+pyvfvM5EfAAAAAABVGEk/AAAAAAB+iqQfAAAAAAA/RdIPAAAAAICfIukHAAAAAMBPkfQ7yWw2Kzw8XFFRUZ4OBQAAAACAUvHIvgrikX0AAAAA4Dk8so9H9gEAAAAAUCWR9AMAAAAA4KdI+gEAAAAA8FMk/QAAAAAA+CmSfgAAAAAA/FQ1TwcAAAAAAICjDMPQ1q1bPR2G1+NKPwAAAADA50ydOlVdu3b1dBhejyv9TjKbzTKbzbJYLJ4OBQAAAACqhIKCAj388MOaN2+ep0PxGSbDMAxPB+HLMjIyFBISovT0dNWrV8/T4QAAAACA3zKZTJ4OwavYk4cyvB8AAAAA4LWSkpL07bffKjQ01NOh+CSG9wMAAAAAvFJ2drY6dOjg6TB8Glf6AQAAAABe6ZtvvvF0CD6PK/0AAAAAAK+QnZ2tlStXauPGjVq/fr1+/vlnT4fk80j6AQAAAABe4Y477tCqVas8HYZfYXg/AAAAAMBjDMPQf/7zH5lMJhJ+N+BKPwAAAADAI06dOqWoqCglJyd7OhS/xZV+AAAAAEClW7p0qRo3bkzC72Yk/f/f2bNndemll+rJJ5/0dCgAAAAA4LeOHz8uk8mkQYMGeTqUKoGk//+bMmWKrr32Wk+HAQAAAAB+bfLkyZ4OoUoh6Ze0b98+7d69W/379/d0KAAAAADgl2bOnKl+/fpp1qxZng6lSvH6pH/NmjUaMGCAWrRoIZPJpKVLl5aoYzab1bp1a9WsWVPR0dHatGmTQ308+eSTmjp1qosiBgAAAAAUt2DBAo0dO1YrVqzwdChVjtfP3p+dna2IiAiNHDlSgwcPLrF+0aJFio+P1+zZsxUdHa3p06erX79+2rNnj5o2bSpJioyM1Llz50q8d8WKFUpISNCVV16pK6+8UuvXr3f79gAAAABAVWAYhvbt26c9e/bo3nvv9XQ4VZbJMAzD00HYy2QyacmSJRo4cKC1LDo6WlFRUZo5c6YkqbCwUGFhYRo7dqzGjx9/0TYnTJigjz/+WIGBgcrKylJBQYGeeOIJTZw4sdT6eXl5ysvLs77OyMhQWFiY0tPTVa9evYptIAAAAAD4gT///FOXXnqpp8Pwe/bkoV4/vL88+fn52rJli2JiYqxlAQEBiomJ0YYNG+xqY+rUqTp8+LAOHjyoV199VQ8++GCZCX9R/ZCQEOsSFhZW4e0AAAAAAH+Rl5dHwu9FfDrpP3nypCwWi0JDQ23KQ0NDlZKS4pY+J0yYoPT0dOty+PBht/QDAAAAAN7q2LFjeu+993T27FlrmWEYuvXWW1WzZk0PRoYLef09/ZVp+PDhF60TFBSkoKAgmc1mmc1mWSwW9wcGAAAAAF6kffv2Sk9P16hRo7Rz50798MMPGjdunKfDQil8Oulv3LixAgMDlZqaalOempqqZs2aubXvuLg4xcXFKSMjQyEhIW7tCwAAAAC8SXp6uvXnDh06eDASXIxPD++vUaOGunTpolWrVlnLCgsLtWrVKnXv3t2DkQEAAACAf9myZYu6du2q+fPnezoUOMDrr/RnZWVp//791tfJyclKTExUw4YN1apVK8XHxys2NlZdu3ZVt27dNH36dGVnZ2vEiBFujYvh/QAAAACqkr59++r06dNuz7XgWl7/yL7Vq1erd+/eJcpjY2Ot3zDNnDlT06ZNU0pKiiIjIzVjxgxFR0dXSnxFw/t5ZB8AAAAAf7NgwQLVqlVLgwcPlslk8nQ4uIA9eajXJ/3ejqQfAAAAgD86dOiQWrduLUkKCwvjyWVeyJ481Kfv6fcks9ms8PBwRUVFeToUAAAAAHC5Hj16WH8m4fddXOmvIK70AwAAAPBHDOf3flzpBwAAAAA47OTJk54OAS5C0u8khvcDAAAA8Fe7du3ydAhwEZJ+J8XFxSkpKUkJCQmeDgUAAAAAXOr777/3dAhwEe7pryDu6QcAAADgb7if3zdwTz8AAAAAAFUYSb+TuKcfAAAAgL/ZvHmz7r77bk+HARdieH8FMbwfAAAAgL9gWL9vYXg/AAAAAOCiTp48qZ49e3o6DLhBNU8HAAAAAADwrCZNmng6BLgJST8AAAAAVFF//vmn9u/f7+kw4EYk/U4ym80ym82yWCyeDgUAAAAAHHb69Gldeumlng4DbsZEfhXERH4AAAAAfMnevXu1dOlSde7cWX379vV0OKgAe/JQrvQDAAAAQBXSrl07SVJQUJCHI0FlYPZ+AAAAAKgCCgsLVXygd15engejQWUh6QcAAAAAP3fu3Dm1b99eAQGkgFUNw/sBAAAAwE9ZLBY9/fTTCg4O1t69ez0dDjyAr3kAAAAAwE/MmjVLl112mfbv3y/DMPTcc8/p9ddf1wsvvODp0OAhzN7vpOKP7Nu7dy+z9wMAAADwOJPJJEnq37+/wsPD9dprr3k4IriTPXkoSX8F8cg+AAAAAJ6wbds2zZkzRxMnTlRoaKik/0v6UTXwyD4AAAAA8FORkZGSpH379mnFihVKTU31bEDwSiT9AAAAAOBDLBaL8vPzra9/+OEHSdLdd9/tqZDgxZjIDwAAAAB8RFZWllq1aqXatWvblD/22GP68ccfPRQVvBn39FcQ9/QDAAAAcLV9+/bJYrHoiiuuUGBgoLWce/ZRnD15KFf6AQAAAMCLzJs3T1deeaXat2+va665Rnv27JFhGNqyZYunQ4MP4kp/BXGlHwAAAICr5OTkqHXr1jp+/LinQ4EPYPZ+AAAAAPBy586dkyRVq1ZNTZs2VVZWlocjgj9heL+TzGazwsPDFRUV5elQAAAAAPiowsJCtW3bVm3atJHFYiHhh8sxvL+CGN4PAAAAwFmnTp1S48aNPR0GfBQT+QEAAACAB/3yyy8aMWKETpw4Ien8iOF///vf1vUvvPCCp0JDFcE9/QAAAADgYn/99Zd+/fVX3XXXXZLOT9D36aefasyYMZKktLQ01apVS2+++aYnw0QVQNIPAAAAAC7Wpk0b6wR9krR//36b9WazubJDQhXF8H4AAAAAcLHiCX+R+fPnV34gqPKYyK+CmMgPAAAAQJHrr79ev/zyi6fDQBVhTx7K8H4AAAAAqCDDMLRhwwYSfngdhvcDAAAAQAUNGzZM1113nafDAErgSr+k1q1bq169egoICFCDBg30008/eTokAAAAAF5u9erVOnjwoPbv36/Fixd7OhygVCT9/9/69etVp04dT4cBAAAAwAesX79evXv39nQYwEUxvB8AAAAAHHDq1Cn169fP02EAdvH6pH/NmjUaMGCAWrRoIZPJpKVLl5aoYzab1bp1a9WsWVPR0dHatGmTQ32YTCb17NlTUVFRWrBggYsiBwAAAOCP7rvvPmVlZXk6DMAuXj+8Pzs7WxERERo5cqQGDx5cYv2iRYsUHx+v2bNnKzo6WtOnT1e/fv20Z88eNW3aVJIUGRlZ6nMyV6xYoRYtWmjdunW65JJLdOzYMcXExKhTp066+uqr3b5tAAAAALzfoUOH1Lp1a3300UdKSEjQsmXLPB0SYDeTYRiGp4Owl8lk0pIlSzRw4EBrWXR0tKKiojRz5kxJUmFhocLCwjR27FiNHz/e4T6eeuopdejQQcOHDy91fV5envLy8qyvMzIyFBYWZtfzEQEAAAD4HpPJ5OkQgFLZk4d6/fD+8uTn52vLli2KiYmxlgUEBCgmJkYbNmywq43s7GxlZmZKkrKysvTjjz+qQ4cOZdafOnWqQkJCrEtYWFjFNgIAAABApdqyZYuWLFkiSVq4cKEeeughHTt2TPPmzdPhw4clnc813n77bT344IOeDBWoMK8f3l+ekydPymKxKDQ01KY8NDRUu3fvtquN1NRUDRo0SJJksVj04IMPKioqqsz6EyZMUHx8vPV10ZV+AAAAAL6ha9eukqQvvvhC99xzjyRp7ty5kqQ6deooMzNTL7/8siZOnOixGAFX8emk3xUuu+wybdu2ze76QUFBCgoKcmNEAAAAAFwlNzdXNWvWLHVdaZOEZ2Vl6dixY3rttdfcHBlQOXx6eH/jxo0VGBio1NRUm/LU1FQ1a9bMrX2bzWaFh4eXOyoAAAAAgOc89dRTqlWrln799ddS13/00Uellrdo0ULp6enuDA2oND6d9NeoUUNdunTRqlWrrGWFhYVatWqVunfv7ta+4+LilJSUpISEBLf2AwAAAMA5r776qqTzt+gWWblypafCATzC64f3Z2Vlaf/+/dbXycnJSkxMVMOGDdWqVSvFx8crNjZWXbt2Vbdu3TR9+nRlZ2drxIgRHowaAAAAgLdYvXq1evfurdWrV3s6FKDSOZX0t2nTxqnHVowbN06PPfaYQ+/ZvHmzevfubX1dNIlebGys5s+fr6FDh+rEiROaOHGiUlJSFBkZqeXLl5eY3M/VzGazzGazLBaLW/sBAAAAUD7DMGzyE8MwdObMGZs6JPyoqkyGYRiOvunnn392qrPWrVvr0ksvdeq93iojI0MhISF2PR8RAAAAgGtlZWWpS5cu6tu3r9566y0dPHhQPXr00F9//eXp0AC3sycPdSrpx/8h6QcAAADca/Xq1apTp47S09OVnp6uxMRE9e7dW71799asWbM0evRoSeev8A8bNkyLFi3ycMRA5bAnD3VqeH9BQYFWr16tmjVrKjw8XI0aNXIqQF/G8H4AAADA/VJTU21u9y3ywgsv6K+//lJhYaFNOX+fA7acutI/YMAANW/eXF9++aXq16+vnJwcderUScuXL3dHjF6NK/0AAABAxS1fvlzvvfeeZs2apcaNG0uS1q1bp9jYWP3xxx+lvmfgwIFaunRpJUYJeBe3Xen/888/9fXXX2vTpk1KTEyU2WzWoUOHnAoSAAAAAPr37y9JCg4O1vz58yVJPXr0KPc9JPzAxQU486aaNWtKkmrUqKH8/HzFxcVp3bp1Lg3M25nNZoWHhysqKsrToQAAAABeb9KkSWrfvr3S0tKsZdu3b1ebNm20YMECa9kHH3wgk8nk1NPCAJTk1PD+BQsWqH///po7d6727Nmj6667Ti+//LL27t3rjhi9GsP7AQAAgP9T9Pi84v9KUkDA+euNkyZN0uTJk2UYhjp16qSdO3d6MlzAp1XK7P3z58/Xzp07dd999+nqq6+uSFM+iaQfAAAAOO+LL77Qo48+qr59+2rBggW6+uqrVadOHYWEhGjZsmU2dZs1a6aUlBQPRQr4Bx7ZVwlI+gEAAIDzGJIPVC578lCH7+k/c+aMTp8+LUk6ceKEvvzyyyo5JId7+gEAAFAVmc1m9enTR9nZ2ZLOXwTjHnzAezmU9L/77rvq0qWLunbtqlmzZmnQoEFatWqVhg0bpnfffdddMXqluLg4JSUlKSEhwdOhAAAAAJVmzJgxWrlypd5++21J0rRp0zwcEYDyODS8/+qrr9bGjRuVk5OjVq1aKTk5WU2aNFF6erp69uypxMREN4bqnRjeDwAAAF935MgRLVu2TPfee69q1qyp1NRU/e9//9Pdd9+t4OBgbd68WVFRUbr++uttntp144036scff/Rg5EDVZk8eWs2RBgMDA1WrVi3VqlVLbdu2VZMmTSRJISEhDOcBAAAAfFRkZKROnjypvXv36pVXXlHPnj21Z88ebdy4Ue+++671ltYLH9NNwg94P4eG91erVk25ubmSpJ9//tlanpWV5dqoAAAAAFSakydPSpJ1hv09e/ZIkr766iuPxQTANRxK+gcPHqygoCBJ56/uFzl79qzmzJnj2si8HBP5AQAAwNu9/vrr+vvf/659+/YpJiZGK1askGEYio2N1aOPPqp+/frpiy++sNbfsWOHzQjekydPMqIX8HEO3dN/zTXXaOvWre6Mx+dwTz8AAAC8VWkJ+6ZNm9StWzcPRAOgNG3bttX+/fudeq9bHtkHAAAAwHcV3a4LwPOWLFmi3bt3KyMjQ/fcc0+Z9Vq0aOF0Hw4l/Tt37lT37t01atQovfHGG/rhhx909OhRpzsHAAAA4DiLxaKhQ4fq1VdfVU5Ojm6//fYSj9A+c+ZMqe+94YYbKiNEAHYYOHCgAgMDVbduXTVr1qzMesOGDXO6D4dm72/Xrp3mzJmjnTt3aseOHXr77be1Y8cOpaWl6aqrrtLatWudDgQAAABASTk5OTKZTKpZs6a17JtvvtHixYu1ePFimUwmff311/r66691zz33KDMzUw0bNtS4ceM8FzQAr+HwI/s6deqkTp062ZTn5uZq165dLg0MAAAAqOoKCgpUt25dVatWTdnZ2QoMDJRk+/SstLQ068+1a9eWdH4oMCNyAd/iwHR7DnFoeP8jjzxSannNmjXVuXNnlwQEAAAAQNq9e7eeeOIJWSwW5eXl6dNPPy213osvvliijIQfQBGHkv7LLrtM1157rXr27KmVK1dKko4dO6Z58+bpH//4h1sC9FY8sg8AAADu1L59e7311lvW1/fee68SEhI8GBEAX+RQ0j927Fg98cQTev3117VkyRKNGDFC7dq108qVK3X77be7K0avFBcXp6SkJD54AQAAUK7CwkItW7ZMJ0+e1MaNG/X888/rzJkz+uabb3T27FlJ0h9//KHVq1fru+++0+nTp8sc5vuvf/1LL730kh566KHK3AQAPsyhe/pr1aqlu+66S5IUGRmpJk2aKCkpSS1btnRLcAAAAICvmzt3rh555BGFhoYqNTVVkjRp0iRJ0p133qnPPvtMl19+ubX+VVddpalTp5ba1g8//KAffvjB/UED8BsOXek/ceKEFi1apK1btyonJ0etW7cm4QcAAADK8eWXX0qSNeEv7vPPP1dBQYFN2e7du/X1119XSmwAfENFJvlz6Er/E088oRUrVuj111/Xrl27lJeXpzvuuEORkZGKjIzUoEGDnA4EAAAAqIpq1KhRouz999/3QCQA/JFDSf/jjz9u8zo5OVk7duzQjh079MUXX5D0AwAAAADgRRxK+i/Upk0btWnTRgMGDHBVPAAAAIBHGYahf/zjH2rZsqVeffXVEut//PFH3XTTTZKkUaNGaerUqWrSpEmJen/88Ycuu+wyt8cLwP+ZTCan3+tQ0h8aGqorr7xSnTp1UseOHa3/NmjQwFpn1qxZevTRR50OCAAAAPCkrVu3atGiRZJUatJflPBL0rvvvlvqvfqSSPgBeAWHkv6jR49qz5491iH9P/zwg5KSkpSTk6MOHTpo2bJlmjt3Lkk/AAAAvEpycrJSU1PVpUsXVa9eXWlpaSooKFBBQYFatGhhUzc/P9/6848//qigoCC1bNlSrVq1KrXtlStXujV2AKgIh5L+wMBAhYeHKzw8XEOGDNGGDRu0bNkyLVmyRKdOnZJUsVkFfYnZbJbZbJbFYvF0KAAAACjHnj17dNVVV0mSGjZsqGPHjtmMVE1MTFRERESp7y1+VX/UqFH65JNPStTJyclxccQA4DoOJf0nT57U999/r2+//Va//fabunTpoptvvlk//vij9T6mpKQkde/eXR06dFCHDh3UsWNHdejQocQ3qL4uLi5OcXFxysjIUEhIiKfDAQAAQBmKHpknSadPn9aJEyds1n/66adlJv3Fvfvuuy6PDQDczeF7+iMiIvTkk0/qo48+UmBgYIk67dq105w5c7Rz507t2LFDb7/9tnbs2KG0tDRdddVVWrt2rcuCBwAAAIo7cuSImjVrVurfqUU2bdpk8/qDDz5QdHS09u3bp06dOmnfvn3uDhMAKo1DSf+0adO0c+dOvfnmm3r88ccVFhamjh07Wpebb75ZgYGB6tSpkzp16mTz3tzcXO3atculwQMAAABFVqxYoX79+unWW2/VN998Yy2/cKK9wYMH27w+duwYj54G4LccSvrj4+NtXicnJ1uv6H/88ce6+eab9cgjj5T63po1a6pz587ORwoAAACUY/r06ZKkb7/91qZ8zZo1HogGABzjrvnxHEr6L9SmTRt16NBBubm5Cg0N1S233KLOnTtr9erV6tWrl4tCBAAAAAAAzqhQ0i9JmZmZ2rVrl7Zv367ExERt3LhRr732mrp3765vvvlGwcHBrogTAAAAKCElJUVPP/20atasqWXLllnLTSaTB6MCAO9R4aT/1KlT1nv67777bknS8ePHNXjwYL3wwgv673//W+EgAQAAgNI89NBD+vrrrz0dBgB4rYCKNtCsWTO1aNFC/fv31/jx4/XJJ5/o1KlTmj59ut5//31XxAgAAIAqZNasWRo1apQKCwu1a9cuDR48WImJicrNzdU999yjhQsXauzYsXrttdeYaR8ALqLCV/r37dunbdu2afv27dq2bZsWL16sgwcPqkaNGiooKNC9996r6OhoRUZGqkePHq6I2eWSk5M1cuRIpaamKjAwUL/++iu3JQAAAHjI6NGjJUmDBg3So48+qsOHD+vrr7/WtGnTtHDhQi1cuNBat3379p4KEwB8QoWT/ssvv1yXX365zaNPMjIy9NNPP2nQoEEyDEMffPCBnnnmGZ09e7ai3bnF8OHD9eKLL6pHjx46ffq0goKCPB0SAACA35gzZ45q166te++9t9x6ubm5mjJlivX1d999p8OHD0uSzp07pxdffLHEe3gkNACUr8JJf8OGDRUZGamIiAhFRESoU6dOqlOnjr777jtdfvnlWrBggSTJYrFUOFh32Llzp6pXr24dhdCwYUMPRwQAAOA/jh07pocffliSNGzYMFWrVvafny+//LJNYv/222/brD916pR7ggQAL1eRyUkrfE//+++/r549e+rQoUN6/vnnFRUVpfbt22vBggV68803rfUCAwOdan/NmjUaMGCAWrRoIZPJpKVLl5aoYzab1bp1a9WsWVPR0dHatGmT3e3v27dPderU0YABA3TNNdfopZdecipOAAAA2MrNzVViYqL1tcViUWJiovbv36/09HT98ssvys7OliTt2bNHkydP9kygAODlKpL0V/hK/8CBAzVw4EDr68zMTB07dkyXXHKJS+6Lz87OVkREhEaOHGlzC0GRRYsWKT4+XrNnz1Z0dLSmT5+ufv36ac+ePWratKkkKTIyUufOnSvx3hUrVujcuXNau3atEhMT1bRpU918882KiopSnz59Khw7AABAVdalSxclJSVZX99+++1asWJFiXp//PGHrrrqqsoMDQC8jrseNVrhpP9CdevWVd26dSVJO3bsUMeOHSvUXv/+/dW/f/8y17/++ut68MEHNWLECEnS7Nmz9e233+r999/X+PHjJcnmG+YLXXLJJeratavCwsIkSbfccosSExPLTPrz8vKUl5dnfZ2RkeHoJgEAAFQJxRN+SaUm/JL0448/VkY4AFAlVXh4/4UyMzM1Z84cdevWTZGRka5u3kZ+fr62bNmimJgYa1lAQIBiYmK0YcMGu9qIiorS8ePHdebMGRUWFmrNmjXlzgI7depUhYSEWJeiLwsAAAB8wTfffKPhw4frpZdeKnUk5IUSEhI0ZcoUFRQUWMv++OMPjR49Wg0aNNBzzz2nF198UZMmTdKQIUP02muv6dNPP3XoitWoUaOc2hYAwMW57Er/mjVr9N577+mLL75Q7dq11aNHD23ZssVVzZfq5MmTslgsCg0NtSkPDQ3V7t277WqjWrVqeumll3TDDTfIMAz17dtXt912W5n1J0yYoPj4eOvrjIwMEn8AAOAzBgwYYP25fv361sfjlaVbt26SpFq1aln/Brr22mt14sQJSSpxH/5nn33mwmgBABVVoaQ/JSVF8+fP13vvvadjx47pjjvu0OLFi9W3b1/t3r271En3vNHFbiEoLigoiEf6AQAAv+DI4+527Nhh/bko4QcAeD+nk/4BAwZo1apV6t27tyZPnqyBAwfaTNznrkkIimvcuLECAwOVmppqU56amqpmzZq5tW+z2Syz2ey1jyIEAACQpE8++UQmk0m//fabNm7caLNu5syZKiwsVLdu3ZSdna0JEyaUOV/RvHnzNG/evMoIGQDgQk4n/d9++63uvvtujRs3Tl27dnVlTHarUaOGunTpolWrVlmfIFBYWKhVq1ZpzJgxbu07Li5OcXFxysjIUEhIiFv7AgAAcEZ6erruvvvucuu8/fbbevvttyspIgBAZXN6Ir/169erVq1auvHGG9WuXTs9//zzOnDggCtjkyRlZWUpMTHROgN/cnKyEhMT9eeff0qS4uPjNXfuXH3wwQfatWuXHn30UWVnZ1tn83cXs9ms8PBwRUVFubUfAAAAZ2VlZXk6BACAnQzDcEu7Tif91157rebOnatjx47pmWee0YoVK3TllVfq2muv1VtvvVViyL2zNm/erM6dO6tz586Szif5nTt31sSJEyVJQ4cO1auvvqqJEycqMjJSiYmJWr58eYnJ/VwtLi5OSUlJSkhIcGs/AACgasnNzVVhYaGOHDkiwzB09uxZ67qzZ8/KMAylp6crNTVVKSkpOn36tFJTU1VQUKD9+/crKytLCQkJOnXqlA4dOuTBLQEAeIMKz94fHByskSNHauTIkdqzZ4/ee+89vfTSS0pNTXXJff29evW66DceY8aMcftwfgAAAHfLyMhQw4YNS8wZ9P777ysyMlLXXHONhyIDAPgqp6/0l6Zdu3Z65ZVX9Ndff+nLL7/Urbfe6srmvQrD+wEAgCulpaXpxx9/LHWS4JEjR+rFF1/0QFQAAF/n0qS/SGBgoAYOHKj//e9/7mjeKzC8HwAAuMpPP/2kBg0aaNSoUWXWqYwnIwEA/I9Tw/vbtGnj1C+ecePG6bHHHnOmSwAAAJ9R2q2JJpNJhmGU+FeSJk2aJEk6depUpcYJAPB/TiX98+fPd6qz1q1bO/U+AAAAX7Fo0SINGzasRPmUKVP05ptv6tlnn9W0adN05MgRh9r94osvXBUiAKAKcSrp79mzp6vj8Dlms1lms7nU++4AAEDVVVrCL0n/+te/JJ0f+QgAQGWp8Oz9VVVcXJzi4uKUkZGhkJAQT4cDAACctHfvXr377rt68skntW/fPi1evFjVqlVTtWrVFBwcrOzsbMXGxqpGjRqaM2eO4uPjtXbtWj3yyCM6ffq0pPNX94cMGaLHH3/cw1sDAIAtkn4AAFClde3aVZmZmdq2bZtWrFhRap1XXnlFTZs21fHjx7Vp0yb9/PPPNuuHDh2qIUOGaPr06ZUQMQAA9nPL7P0AAAC+IjMzU5L066+/llvv+PHjkqS1a9e6PSYAAFyFpN9JZrNZ4eHhioqK8nQoAABUOV999ZVatWqldevW6c0339Rll12mgwcPllm/sLBQN9xwg4YOHSpJOn36tNq1a6eJEyda62RkZNjVd2FhYanlPFIPAOCNGN7vJO7pBwDAcwYOHChJ6tOnj3JzcyVJTz/9tBYvXlxq/d9//916hX7RokWaPn269u7dqxdeeKFS4gUAwFO40g8AAHxWUcIvSefOnSuznmEYNq/z8/PdFhMAAN6EpB8AAHilb7/9Vq1bt9aaNWskSb/99pvatGmjTz75pNT6S5YskclkKnXp3LmztZ7JZNLLL79cKdsAAICnMbwfAAB4pdtuu02SdNNNN6mgoEBDhgzRwYMHdffdd3s4MgAAXO/CUWmuwpV+JzGRHwAAlaNo2H7xofwAAMA+JP1OiouLU1JSkhISEjwdCgAAXsswDH388cfavn27Tflnn32mvn376ty5c/r666/14YcfymQyqWfPnsrJybE+Hq+IyWTSX3/9VZmhAwDgFxjeDwAA3Ob777/XfffdJ8l22OKQIUMkSddff702btxoLV+zZo0mTZqkadOmVW6gAAD4Ka70AwAAGzk5OXrzzTe1f/9+p96flJSkmTNnqqCgQNu2bSu3bvGEv8j333/vVL8AAKAkrvQDAAAbzz//vP773/9q3LhxTk0q1KFDB0nn78U3mUyuDg8AADiApB8AANgoekRekcOHD+vAgQPq1auXJGndunW65JJL1KZNG+3atUu//fabEhISFB4eroyMDOv7nn32WeXk5Fhfm0wmTZo0Sc8991y5/V94/z8AAHAeSb+TzGazzGazLBaLp0MBAMClLry636pVK0nnvwxo0KCBevToYa0XHh5eZjvFE/4iF0v4AQBASRUZOcc9/U5i9n4AgLcrb2i+YRg264u/vrC8yLp162x+77nrecIAAMB1SPoBAPBD27dvV7NmzfTOO++UWPfBBx8oICBAAQEBMplMSk1Ntb4+fPiwfv31V2vdgID/+1Ph2Wef1ciRI0tdBwAAvBO/rQEA8EMjRozQ8ePH9cgjj5RYN3z4cJvXr7zyivXn6667zt2hAQCASkTSDwCAj9q8ebP+/PNP6+uTJ0/qrbfe0v79+1VYWGgt/+uvvyRJW7du1ebNm0u08/rrr1t/Pnz4sBsjBgAAlY2J/AAA8EH79u1TVFSUpP+7t75JkybW9Q0bNrT+HBYWpkOHDqlLly6VGyQAAPA4rvQDAOCDtm7dWu7606dP27zeuXOnO8MBAABeiqQfAAAPOHr0qDZt2mR9vX37dv3xxx8l6m3evFnLly/XqFGjlJSUpA8//FCPP/64hg0bZq3z2WefXfRRPrfccovrggcAAC5XkcfylYfh/QAAeMAll1wiSUpMTFSzZs0UEREhyfYxeDt37rQO4Zek9957r9S2hgwZ4sZIAQCAL+NKPwAAHrR+/Xrt37+/1HXFH50HAAD8W/Ev/l2JpN9JZrNZ4eHhNldgAABwhrt+yQMAAJD0OykuLk5JSUlKSEjwdCgAADdbuHChrrvuOq1YsULR0dFasWJFiToffvihrr/+eqWmpuqtt96SyWSy3pv3zDPPaNCgQSosLNT333+v6Oho6/tGjx6tHj16WF8Xvc9kMmnUqFHu3zgAAODXuKcfAICLuOeeeyRJ/fr1s/574dX52NhYSdKzzz6r999/31p+7NgxvfLKK5KkX375RTfffHNlhAwAACCJK/0AAFgZhqEBAwbooYcecrqN9PR0m9eFhYXWn/Pz851uFwAAwBkk/QAA/H/bt2/XN998o7lz53o6FAAAAJcg6QcAVCnffPONhg0bprS0NPXp00cmk0n//ve/FRsbq5ycHGs9k8mk++67T3379i21neL33hd/ru4XX3xhU69ly5bWn2NiYly8NQAAoCoo/reGo7inHwBQpQwYMEDS+aH2K1eulCRNmTJFkhQSEmJT9+OPP67c4AAAAFyMK/0AAJ9jGIaeeeYZbdy4UZK0e/dubdiwwbru888/19y5c5WXl6ekpCT95z//0cmTJ23aWL58eYl233rrLfcHDwAA4KCKPN6XK/0AAJ8zbtw4zZgxQ6+88ooMw1D79u0lSYcOHVJCQoLuuusuSdKuXbv0xhtvSJJefPFFm1+YxYfyAwAA+Ksqf6V/z549ioyMtC61atXS0qVLPR0WAKAcM2bMKLV8//79+v77762v33vvvcoKCQAAwCtV+aS/Xbt2SkxMVGJiotatW6fg4GD16dPH02EBgM/bsmWL7rnnHh06dKjceoZh6J///KfmzJkjSXr66adLHWbftm3bEpPmXeimm26ymXk/IyPDZn1FJsEBAADwRQzvL+Z///ufbrrpJgUHB3s6FADweV27dpUk/fHHH9b77UuzevVq65X7qKgoTZs2TZI0duxYa50dO3bowIEDpb7/Yl8qAAAAVGVef6V/zZo1GjBggFq0aCGTyVTq0Huz2azWrVurZs2aio6O1qZNm5zqa/HixRo6dGgFIwYAFLd79+5y16elpVl/vvDKfJHMzMwy35+VleVUXAAAAFWB1yf92dnZioiIkNlsLnX9okWLFB8fr0mTJmnr1q2KiIhQv379dPz4cWudyMhIdezYscRy9OhRa52MjAytX79et9xyi9u3CQD8wdGjR3X//ffrwQcfVHx8fJmzyhZP6jdv3iyTyaQXX3xRkhQXF6fBgwdb1/fq1cv683333aeQkBCZTCb97W9/KzOOjh07VmxDAAAA/JjJqMjc/5XMZDJpyZIlGjhwoLUsOjpaUVFRmjlzpiSpsLBQYWFhGjt2rMaPH2932x999JG+//77iz6TOS8vT3l5edbXGRkZCgsLU3p6uurVq+fYBgGAD+vbt69++OEH6+uff/5ZN9xwg/V18fvni37VFC/bv3+/2rZtWwmRAgAAeKfi6fi4ceP05ptvllrvqaeest4CWZw9eajXX+kvT35+vrZs2aKYmBhrWUBAgGJiYsq9f7Q09g7tnzp1qkJCQqxLWFiYw3EDgD/Ys2ePzeszZ8449P7yhuwDAADANXw66T958qQsFotCQ0NtykNDQ5WSkmJ3O+np6dq0aZP69et30boTJkxQenq6dTl8+LDDcQOAJ1ksFiUlJckwDOXk5Gj//v1l1t27d6/mz58vwzD0+++/a8eOHTpy5Ih+/fVX/fnnnzZ1d+3apVdffVWZmZnWmfiL5Ofna9GiRTZlnTt3dt1GAQAAoFTM3i8pJCREqampdtUNCgpSUFCQmyMCAPcZNWqU5s+fr2nTpmnu3Lnau3ev1q1bp+uuu86mXmZmptq1aydJGjFixEXbnTBhgqTzw88uxOcmAACAZ/j0lf7GjRsrMDCwRMKempqqZs2aubVvs9ms8PBwRUVFubUfAHBUfn6+Nm/erMLCwlLXz58/X5L0wgsvaO/evZKkTz/9tES95ORkt8UIAACAyuHTSX+NGjXUpUsXrVq1ylpWWFioVatWqXv37m7tOy4uTklJSUpISHBrPwDgqNjYWEVFRWnKlCl2v6f4BHvllQEAAMC3eP3w/qysLJv7TZOTk5WYmKiGDRuqVatWio+PV2xsrLp27apu3bpp+vTpys7OtmsoakWYzWaZzWZZLBa39gMAFyosLFRmZqZCQkJKXV901f65557Tf/7zH6Wlpal+/fol6mVkZFh/PnTokA4fPqwTJ06oXr16+uuvv7Rp0ya3xA8AAADHVOhijOHlfvrpJ0NSiSU2NtZa56233jJatWpl1KhRw+jWrZvx66+/Vlp86enphiQjPT290voEULX179/fkGTs2LGj1PXFPysff/xxQ5KxZMmSUtezsLCwsLCwsLB4binun//8Z5n1nn766VLL7clDvX54f69evWQYRoml6J5USRozZowOHTqkvLw8bdy4UdHR0Z4LGADcbNmyZZJUYob80rzxxhuSpCeeeMKtMQEAAMA7eX3S762YyA/AhbKysjR06FB9/vnnZdZ59913FRsbq3PnzlnLJk+erPHjx1tfb968WYMGDbJOsrdy5Ur9/e9/14cffmgztGvGjBmKiopSSkqKDMPQY489ppdffrnUfv/44w/dfffd3KcPAABQ1Tg5uhT/H8P7ART597//XepQreKK1n/00UeGYRhGTk6Oteyvv/6yqXPllVfavC5rGTRokLFt2zaPD09jYWFhYWFhYWFxbCmuyg7vBwBvVTShXl5enrKzs5WSkmKz/vTp08rNzVVeXp6k84/SK3L8+HHl5OTYTAaakpKiM2fOWF/v3btXaWlpF43jp59+KtE3AAAAIDG8HwCc1rt3b9WrV081a9ZUnTp19O6771rX/f7772rUqJFq1aqlRo0aKS8vT0FBQdb1TzzxhGrXrq1Tp05Zy7p27aqGDRva9NGgQYOLxpGWlqZ+/fq5YIsAAADgb0j6ncQ9/QDWrFlT5roHHnjA+nN2drZ+//33UusVTcoHAAAAuANJv5Pi4uKUlJSkhIQET4cC4P87e/as3XVzcnJkGIb1PRaLxToM/8I2DcNQamqqCgoKrGXFr9CX5siRIzavq1WrVmq906dP2x0zAAAA4CiSfgB+YdGiRQoODtaMGTMuWvfAgQOqXbu2AgICFBwcrOXLl+vqq69WgwYNbL44+PHHHxUcHKyAgAA1a9ZMNWrU0JIlSxQcHKzGjRuX28fRo0dtXnfu3LnUes8++6wdWwcAAAA4h6QfgNdbvXq1Nm/eXG6dYcOGSZL++c9/XrS9mTNn2rwuGrmTk5OjrVu3WstLa+vOO++0J2QAAADAIecf2uR6pY83xUWZzWaZzWabmbcBuF5KSop69+4tyXUfhBe2U/zZ9Rfrw10fxgAAAIA7kPQ7KS4uTnFxccrIyFBISIinwwH81oXD5IvMnTtXubm5OnLkiLp27Wqz7tSpU3rllVd05513KioqSl9//bU+//xzffjhh6W2deDAAevPN9xwQ7nxkPQDAADAHYpfiHIlkn4AXi0g4P/uQjIMQyaTSWfOnNFDDz1U5nuK7rd/5ZVXZBiGbr/9drfHCQAAAHgj7ukH4NWKf+P56KOP6siRI5o1a5YHIwIAAAAqV0VGAXClH4BXK/4B984772jnzp1at26dByMCAAAAfAdX+p1kNpsVHh6uqKgoT4cC+CyLxaK3335bO3bsKLFu//79euutt5Sfn29T7mjCHxoaWqEYAQAAAF/GlX4nMZEfUHHvvvuu4uLiJJWcIO+KK66QJA0ePLhCfRw/frxC7wcAAAB8GVf6AbhFZmamCgoKylxvGIZWrlxpU3bs2DGdPXtWf/zxh7Xsyy+/dFuMAAAAgL/jSj8Al0tPT1f9+vXVunVrJScnl1pnxIgR+vzzz62v9+7dq3bt2lVWiAAAAECVwJV+AC63ceNGSdLBgwfLrPPBBx/YvH7++efdGRIAAADgsy68FdYRJP0AyvXxxx9r69at1tdr165VzZo19frrr+u3335TdHS0lixZojlz5mj27NkaN26cxo0bZ61vMplkMpk0Z84cRUVFKTMzUytWrCjRz/LlyytjcwAAAIAqheH9TjKbzTKbzbJYLJ4OBXCbH3/8Uffdd5+k//t28YYbbpAkPfHEE9Z69ky29/DDD0uS6tWrV+r6U6dOVShWAAAAACVxpd9JcXFxSkpKUkJCgqdDAdxm586dng4BAAAAQAVwpR+own755RedOnVKAwYM0IcffqgjR44oJydHMTExWrt2rf7zn/9Y6548eVJffPGFB6MFAAAA4CiSfqAKu/766yVJL730kp599llr+Ysvvlii7u23364NGzZUWmwAAABAVVKRyfrKw/B+wM3++usv9evXT99++62nQynTkiVLLlqHhB8AAADwPST9gJs9+uijWrFihW677baL1i36dq+0b/kMw7ApLywsLLX+xdoomnyysLDQWh4QwEcBAAAA4I/4Sx9ws5SUFLvqPfPMM2rZsqUGDRqkyy+/XJmZmdZ1ZrNZAQEBCggI0LRp0/SPf/xDgYGBCggI0KZNm3T11Vfr73//u7Zt26amTZvqtdde05VXXqnhw4db23jttdcUEBCgatWqyWQyKTAw0Lpu48aNLtteAAAAAK5lMpmcf6/hrhsHqoiMjAyFhIQoPT29zEeRoWqLiorS5s2bJZV/n86F/5Fnz55tfcydvf/JO3furN9++82mrKjPinxQAAAAAHC94vnBP//5T82YMaPUeuPHj9d///vfEuX25KFc6QfcrDKT7eJD9gEAAACApB/wIwzcAQAAAFAcSb+TzGazwsPDFRUV5elQ4OUSEhJsXhcWFurWW29VXFycJOnhhx8udTTAI488IpPJ5NBIge3bt5coc7QNAAAAAP6DpN9JcXFxSkpKKpHQAcUVn4yvSEJCgr777ju9/fbbkqQ5c+ZUdlgAAAAAqohqng4AqAzr16/X7t27NXLkSKfef+7cOc2aNUu9e/dWx44dlZycrKefflq//fab2rVrp9tvv12XXHKJNmzYoD179uiqq65SVlZWiUn1JKmgoMD6M1fgAQAAALgTST+qhOuuu06SdMUVV6hHjx4Ov3/WrFl67LHHJJ2/b75jx446e/asJOnAgQP67rvvXBcsAAAAALgIw/tRpezdu9ep923atMnmdVHCDwAAAADejKQffuX06dMKDAyUyWRS9erV9frrr9usL3qkXX5+vvr06aPnn39er7zyijp16qTw8HDrpHcRERFauHChTCaT4uPjbR6FV5Eh+fXq1XNqpAEAAAAAOIPh/fArL774ojVBP3funJ544gnFx8db1xet+/zzz7Vy5UqtXLmy1Ha2b9+ue+65R5L0xhtvaNiwYS6Jr7SJ/QAAAADAXbjSD7+SkZFR7vqipD8vL8+hdotf6QfgPk8++aSnQwAAAPArJP3wWj/88INMJpMaNWqktLQ0SVJ2drYuu+wymUwmzZ8/X71799aMGTO0Z88ede/eXd98802Jdt555x3rz6NHj5bJZHJ4Fv/FixdXaFsA2Mfe219atmzp5kgAAAAql2EYbmmXpF/nh2936NBB4eHheuyxx9y2s+GYvn37Sjp/n/4rr7wiSTKbzUpOTpYkjRgxQqtXr9Y///lP3Xvvvfr111+Vmppaop1HHnmk8oIGAAAAAC9S5ZP+EydOaObMmdqyZYt+//13bdmyRb/++qunw8IFsrKyJJV9T/ypU6cqMxwAAAAA8AlM5KfzE77l5uZKkgoKCtS0aVMPR+SdDh06pL1796pPnz4l1m3fvl25ubnq1q1buW2sW7dOTZo0UW5urvLz8xUVFaXCwkItW7ZM11xzjbZs2aKvvvqqxMR5b731lvbs2VPmkN6iq/8AAAAAgGIML/fzzz8bt912m9G8eXNDkrFkyZISdWbOnGlceumlRlBQkNGtWzdj48aNDvUxY8YMo27dukaDBg2MCRMmOPTe9PR0Q5KRnp7u0Pt8kSRDkvHTTz/ZlBcWFlrXnTp1qsz379+/31qvaDlz5ozxwQcflChnYWGpmstXX31lV72WLVt6PFYWFhYWFhYWloouxY0dO7bMehMmTCi13J481OuH92dnZysiIkJms7nU9YsWLVJ8fLwmTZqkrVu3KiIiQv369dPx48etdSIjI9WxY8cSy9GjR3XmzBl98803OnjwoI4cOaL169drzZo1lbV5Pmn9+vU2r41icyCUdk99kaSkpBJlKSkpWrZsmeuCAwAAAABYef3w/v79+6t///5lrn/99df14IMPasSIEZKk2bNn69tvv9X777+v8ePHS5ISExPLfP9nn32mtm3bqmHDhpKkW2+9Vb/++qtuuOGGUuvn5eXZPO7tYo+I80cmk8nmdfHH2RkOToJosViYOBEAAAAA3MTrr/SXJz8/X1u2bFFMTIy1LCAgQDExMdqwYYNdbYSFhWn9+vXKzc2VxWLR6tWr1a5duzLrT506VSEhIdYlLCyswtvha44cOaJDhw5JOp+0f/LJJ9Z106dPV+/evTV69Gj9+uuvmjlzplauXKmNGzdq0aJFJdrq37+/9u/fX2mxAwAAAEBV4vVX+stz8uRJWSwWhYaG2pSHhoZq9+7ddrVx7bXX6pZbblHnzp0VEBCgm266SbfffnuZ9SdMmKD4+Hjr64yMjCqX+JvNZpnNZmVlZenxxx/X3LlzreuKfl69erVmzZp10bYOHz6sw4cPuy1WAAAAAPAFF46odhWfTvpdZcqUKZoyZYpddYOCghQUFOTmiHzD8ePHbRJ+AAAAAIB38enh/Y0bN1ZgYGCJyeNSU1PVrFkzt/ZtNpsVHh6uqKgot/bjDf7973+X+q3TZZdd5oFoAMDx+UMAAACqKp9O+mvUqKEuXbpo1apV1rLCwkKtWrVK3bt3d2vfcXFxSkpKUkJCglv78Qb2joIAAAAAAHgXrx/en5WVZTPRW3JyshITE9WwYUO1atVK8fHxio2NVdeuXdWtWzdNnz5d2dnZ1tn83aXovnaLxeLWfjytKj6dAAAAAAC8SUVGOXp90r9582b17t3b+rpoEr3Y2FjNnz9fQ4cO1YkTJzRx4kSlpKQoMjJSy5cvLzG5n6vFxcUpLi5OGRkZCgkJcWtfnuTP2wYAAAAA/s7rk/5evXpd9FuNMWPGaMyYMZUUEQAAAAAAvsGn7+n3pKo0kR+qppYtWyorK8tt7U+cOLFE2YVf8H3wwQcV6uP6668vc933339f7nsNwygRT3R0dIXikaRrrrlGDz30kEPv6dKlS4X7BQAAQNVE0u+kqjCR3+HDhz0dAjzIXc8JBQAAAFCSu55ORNKPMv3yyy+eDgEe5unEv6L9ezp+AAAAwNNI+lEmnoNdtflDwuzqbeD/BAAAAHwNSb+TqsI9/cUflYiqx91Jvz3tBwTwESX5xxcwAAAAcF5F/h7kL2onVYV7+kubaA1VB4kmAAAA4PtI+gGUicQf3opbLQAAAOxD0g+gVN6Q8HtDDAAAAIAvI+l3UlW4px9Vmzfc08/s/QAAAEDFkPQ7qSrc04+qzRuSfgAAAAAVQ9IPoFQmk4n7pgEAAAAfR9IPoFTecCXeG2IozhVfgnjbNgEAAMC/kfQDKJOnr/T76z39nt6vAAAAqDpI+p3ERH7wdyaTSYWFhW5t3928NekHAAAAKgtJv5OqwkR+AQGcHlVZjx49VLNmTbe1Hx0dfdE6V199tdv6BwAAAKoCsjqU6bbbbvN0CF5hxIgRatq0aYnyKVOmqE+fPg63V7t2bTVp0uSi9d58880y1910002aMmVKqesuvfRSxcXF6a677iqRNF966aWSpNdee02vvvpquf33799f1atX1+bNm9W/f38tXLhQY8aM0fr167V8+XJ9+umnevzxx3XLLbdow4YN+vzzz7Vq1Spt2bJF69at05133mlta+XKlRo1apReeeUVTZ8+Xe+++66uv/566/ohQ4Zo7969kqQff/xRf/vb37Rp0ya1b99eP/74Y/k7CgAAAEDZDFRIenq6IclIT0/3dCgud/vttxuSfGaJiIhwS7uGYRhvvvmmTdmQIUMMwzCM/Px8h9tr27at8cADD9jV7y233FLqul27dhmGYVhf//3vf7f+/PDDD9scx+eee86mzeLK6/+LL76o0PmTkpJibSslJaXE+uzsbOv6zZs3l9vWhbG1b9/ern3du3fvMtd9//33F93/F/YdFRVV4fOpS5cuxoMPPujQe1zRr68sX331lV31WrRo4fFYWVhYWFhYWFgquhQ3ZsyYMutNmDCh1HJ78lCu9KNMBpONXZSz94y7c99yHzsAAACAIiT9KJM7J3FDxZSX2JP0/x9X7wu+CPMeHAsAAAD7kPQ7qSrM3u9rf1ST7AIAAADwRxXJdUj6nVQVZu/3taTfncr6T+buLxo4BqhqOOcBAABci6QfZapbt66nQ3CIL13pr2qJzcWOjS8dO0+oaucLAAAAXIekH2WaOHGi9efk5GQPRuJ9SFJ9A8cJAAAAVR1JP/wGV0PPuzDRrcr7haQfAAAAVR1JP/yGJxI8TyWVzN4PAAAAwB4k/YAdePQbJNcdN44/AAAAKgtJP+DFSA4BAAAAVARJP+ziC0PGKzPGor58Yb/4I/Y7AAAAYB+SfieZzWaFh4crKirK06EAKIO/fDngL9sBAACAsrnrbz6SfifFxcUpKSlJCQkJng4F/19hYaHb2vblpItbBOCPOK8BAADsQ9IPp1x//fUOv2f8+PF21Rs0aFCZ66pXr27zeuXKldafx44da/25Vq1aNq8l6aOPPrpo3506dbL+XKdOHT377LOSpPvuu09hYWEXfX+bNm10zz336Ouvvy6zzscff6znnntOjRs3vmh7ZfG32ft9MWYAAACgslTkggdJP5yydu1anTt3Tvv27bOWZWZm6ty5c7JYLMrLy5PFYrG+tlgsmjp1qk1ZkX79+ik/P1/nzp3TuXPn9OWXX6p///4l+jQMQ7m5udb2LBaLbrrpJhUWFspisahevXo2scyYMUPLli2zlg0ZMsSm3zvuuMOmfYvFopiYGOvr9PR0TZkyRZJUr149HTx4sERMJpNJtWvXtr7ev3+/Pv74Y912220ltr9oiY6OVqtWrZSamqqJEyfas7sdQgINAAAAoEg1TwcA3xUYGGiTYAYEBCgwMFCSVKNGjVLfExBQ8numgICAElfwy0pcS3u/yWQqUb+o3oX1i78ub93FXhfv78J9UF6s5bVfGk8OYfaHLw/8YRsAAABQNbjrb3+u9MMu9iRPziZY3pSYcZ+wf3H1ucX5AQAAAF9D0g+XqaoJUVXdbgAAAACVoyIXs0j64dfKS8i9YYSBs18YeEPsjvC1eL0NXywBAADAWST9cBlvSuy8KZbKduG2kzACAAAAVRdJP+AEb/5SwZtjw/njwxcxAAAAqCwk/ZJeffVVdejQQR07dtTHH3/s6XDgJH9MpPxxm0rjri8q+AIEAAAAVV2Vf2Tf77//roULF2rLli0yDEO9e/fWbbfdpvr163s6NK9S2cmTO5JdEkAAAAAAVU2Vv9K/a9cude/eXTVr1lStWrUUERGh5cuXezqsKoVkHL7CUyMv+D9SUlUZBQMAAFBRXp/0r1mzRgMGDFCLFi1kMpm0dOnSEnXMZrNat26tmjVrKjo6Wps2bbK7/Y4dO2r16tVKS0vTmTNntHr1ah05csSFWwCUrarM3g8AAADAM7x+eH92drYiIiI0cuRIDR48uMT6RYsWKT4+XrNnz1Z0dLSmT5+ufv36ac+ePWratKkkKTIyUufOnSvx3hUrVig8PFyPPfaYbrzxRoWEhOjaa69VYGCg27fLH3HlDd6GL0cAAABQ1Xl90t+/f3/179+/zPWvv/66HnzwQY0YMUKSNHv2bH377bd6//33NX78eElSYmJiuX08/PDDevjhhyVJo0aN0hVXXFFm3by8POXl5VlfZ2Rk2Lsp8AH2fnFRPJn0ti87eGTf/yHpBwAAQFXn9cP7y5Ofn68tW7YoJibGWhYQEKCYmBht2LDB7naOHz8uSdqzZ482bdqkfv36lVl36tSpCgkJsS5hYWHOb4Cf8cYEq7yE1xvjvZAzCbsvbBcAAACAyuHTSf/JkydlsVgUGhpqUx4aGqqUlBS727njjjsUHh6ue++9V/PmzVO1amUPgJgwYYLS09Oty+HDh52O35eYTCZdfvnlkqT27dtby+vUqWP9OSDAp08n+BC+2AAAAADs4/XD+yuDI6MCgoKCFBQUJLPZLLPZLIvF4sbIvMsPP/ygN998U48//ri1LDQ0VG+99ZZq1aqloKAgD0bnm8PYfTFme/nztgEAAAC+wqeT/saNGyswMFCpqak25ampqWrWrJlb+46Li1NcXJwyMjIUEhLi1r68RZs2bTR9+vQS5WPGjHF5X+5IGP3p6rA/bYvkf9sDAAAAOMpdF818ejx2jRo11KVLF61atcpaVlhYqFWrVql79+4ejAyO8MWEzxdjdpQ/bKOrt4HRCwAAAPA1Xn+lPysrS/v377e+Tk5OVmJioho2bKhWrVopPj5esbGx6tq1q7p166bp06crOzvbOpu/u1TF4f3wDd44e78/fIHgSd5wDAEAAOA5Ffl72uuT/s2bN6t3797W1/Hx8ZKk2NhYzZ8/X0OHDtWJEyc0ceJEpaSkKDIyUsuXLy8xuZ+rVcXh/ah8zN7vn0jiAQAAUFm8Punv1avXRf9AHjNmjFvuK8f/IZEE4E344gQAAMA+Pn1PvyeZzWaFh4crKirK06HAhzmbuPAljH38ZT/5y3YAAACg8pH0OykuLk5JSUlKSEjwdCgoh6NJtb31iydhXHH0XiTLAAAAqOpI+uE3HE2+SQgBAAAA+DuSfngld1w9J8n3Xr5ybBjVAQAAAF9D0u8k7ul3HV9J+DzBFbP3k6gCAAAAVRdJv5Oq2j39JObuwYgGAAAAAO5E0g84wdOJtaf79xXsJwAAAFR1JP0AAAAAAPgpkn4ncU8/AAAAAMDbkfQ7qard0w8AAAAA8D3VPB2AryuaiC0jI8PDkbheVlaW9efMzEy3bWNBQUGJts+dO1ei3sX6P3v2bIm6F5YFBgba9Hth+3l5eXb1l5+fb11ffDI+R/dR8f4ulJGRUep+kEoej+LbkpeXZ7PO3m260NmzZyt0zDMzM21+rlGjRon2i2RlZTnUl8Visavehce4uOzs7HLfW1o89vZbHovFovz8fIffU1UUPy/KU1hY6OZIAAAA3K/435zl/Y1YVt5gz8TgJoPneVXIX3/9pbCwME+HAQAAAACoYg4fPqyWLVuWW4ekv4IKCwt19OhR1a1b1y9nCo+KiuIWBif4837zpW3zplg9EUtl9emuflzZbkZGhsLCwnT48GHVq1fPJW3C93nTZ4Qv8ef95ivb5k1xeiqWyujXnX3wOw4VZRiGMjMz1aJFCwUElH/XPsP7KyggIOCi36z4ssDAQD48nODP+82Xts2bYvVELJXVp7v6cUe79erV85pzAp7nTZ8RvsSf95uvbJs3xempWCqjX3f2we84uEJISIhd9ZjID+WKi4vzdAg+yZ/3my9tmzfF6olYKqtPd/XjTccP/olzzDn+vN98Zdu8KU5PxVIZ/bqzD286hvB/DO8HAPi9jIwMhYSEKD09nasgAAC/wu84XAxX+gEAfi8oKEiTJk1SUFCQp0MBAMCl+B2Hi+FKPwAAAAAAfoor/QAAAAAA+CmSfgAAAAAA/BRJPwAAAAAAfoqkHwAAAAAAP0XSDwAAAACAnyLpBwBUaYcPH1avXr0UHh6uq6++Wp999pmnQwIAoMLS0tLUtWtXRUZGqmPHjpo7d66nQ4KH8Mg+AECVduzYMaWmpioyMlIpKSnq0qWL9u7dq+DgYE+HBgCA0ywWi/Ly8lS7dm1lZ2erY8eO2rx5sxo1auTp0FDJqnk6AAAAPKl58+Zq3ry5JKlZs2Zq3LixTp8+TdIPAPBpgYGBql27tiQpLy9PhmGI671VE8P7AQA+bc2aNRowYIBatGghk8mkpUuXlqhjNpvVunVr1axZU9HR0dq0aVOpbW3ZskUWi0VhYWFujhoAgPK54vdbWlqaIiIi1LJlSz311FNq3LhxJUUPb0LSDwDwadnZ2YqIiJDZbC51/aJFixQfH69JkyZp69atioiIUL9+/XT8+HGbeqdPn9b999+vOXPmVEbYAACUyxW/3+rXr69t27YpOTlZCxcuVGpqamWFDy/CPf0AAL9hMpm0ZMkSDRw40FoWHR2tqKgozZw5U5JUWFiosLAwjR07VuPHj5d0fthjnz599OCDD+q+++7zROgAAJTJ2d9vxY0ePVo33nij7rzzzsoKG16CK/0AAL+Vn5+vLVu2KCYmxloWEBCgmJgYbdiwQZJkGIaGDx+uG2+8kYQfAOAT7Pn9lpqaqszMTElSenq61qxZo3bt2nkkXngWST8AwG+dPHlSFotFoaGhNuWhoaFKSUmRJP3yyy9atGiRli5dqsjISEVGRur333/3RLgAANjFnt9vhw4dUo8ePRQREaEePXpo7Nix6tSpkyfChYcxez8AoEq7/vrrVVhY6OkwAABwqW7duikxMdHTYcALcKUfAOC3GjdurMDAwBITF6WmpqpZs2YeigoAgIrh9xscQdIPAPBbNWrUUJcuXbRq1SprWWFhoVatWqXu3bt7MDIAAJzH7zc4guH9AACflpWVpf3791tfJycnKzExUQ0bNlSrVq0UHx+v2NhYde3aVd26ddP06dOVnZ2tESNGeDBqAADKx+83uAqP7AMA+LTVq1erd+/eJcpjY2M1f/58SdLMmTM1bdo0paSkKDIyUjNmzFB0dHQlRwoAgP34/QZXIekHAAAAAMBPcU8/AAAAAAB+iqQfAAAAAAA/RdIPAAAAAICfIukHAAAAAMBPkfQDAAAAAOCnSPoBAAAAAPBTJP0AAAAAAPgpkn4AAAAAAPwUST8AAPB7+fn5atu2rdavX+/SdpcvX67IyEgVFha6tF0AAFyFpB8AAB8zfPhwmUymEsv+/fs9HZrXmj17ttq0aaO//e1v1jKTyaSlS5eWqDt8+HANHDjQrnZvvvlmVa9eXQsWLHBRpAAAuBZJPwAAPujmm2/WsWPHbJY2bdqUqJefn++B6LyLYRiaOXOmHnjgAbe0P3z4cM2YMcMtbQMAUFEk/QAA+KCgoCA1a9bMZgkMDFSvXr00ZswYjRs3To0bN1a/fv0kSTt27FD//v1Vp04dhYaG6r777tPJkyet7WVnZ+v+++9XnTp11Lx5c7322mvq1auXxo0bZ61T2pXx+vXra/78+dbXhw8f1pAhQ1S/fn01bNhQd9xxhw4ePGhdX3QV/dVXX1Xz5s3VqFEjxcXFqaCgwFonLy9PzzzzjMLCwhQUFKS2bdvqvffek2EYatu2rV599VWbGBITE8sd6bBlyxYdOHBAt956q4N7WTp48GCpoyp69eplrTNgwABt3rxZBw4ccLh9AADcjaQfAAA/88EHH6hGjRr65ZdfNHv2bKWlpenGG29U586dtXnzZi1fvlypqakaMmSI9T1PPfWUfv75Z3311VdasWKFVq9era1btzrUb0FBgfr166e6detq7dq1+uWXX1SnTh3dfPPNNiMOfvrpJx04cEA//fSTPvjgA82fP9/mi4P7779fn3zyiWbMmKFdu3bpnXfeUZ06dWQymTRy5EjNmzfPpt958+bphhtuUNu2bUuNa+3atbryyitVt25dh7ZHksLCwmxGU/z2229q1KiRbrjhBmudVq1aKTQ0VGvXrnW4fQAA3K2apwMAAACO++abb1SnTh3r6/79++uzzz6TJF1xxRV65ZVXrOtefPFFde7cWS+99JK17P3331dYWJj27t2rFi1a6L333tPHH3+sm266SdL5Lw5atmzpUEyLFi1SYWGh3n33XZlMJknnE/L69etr9erV6tu3rySpQYMGmjlzpgIDA3XVVVfp1ltv1apVq/Tggw9q7969Wrx4sX744QfFxMRIki677DJrH8OHD9fEiRO1adMmdevWTQUFBVq4cGGJq//FHTp0SC1atCh13T/+8Q8FBgbalOXl5VlHBQQGBqpZs2aSpNzcXA0cOFDdu3fX5MmTbd7TokULHTp0yIG9BQBA5SDpBwDAB/Xu3VuzZs2yvg4ODrb+3KVLF5u627Zt008//WTzJUGRAwcOKCcnR/n5+YqOjraWN2zYUO3atXMopm3btmn//v0lrqjn5ubaDH3v0KGDTaLdvHlz/f7775LOD9UPDAxUz549S+2jRYsWuvXWW/X++++rW7du+vrrr5WXl6e77rqrzLhycnJUs2bNUte98cYb1i8XijzzzDOyWCwl6o4cOVKZmZn64YcfFBBgO1iyVq1aOnv2bJkxAADgKST9AAD4oODg4DKHsxf/AkCSsrKyNGDAAL388ssl6jZv3tzuWf9NJpMMw7ApK34vflZWlrp06VLqTPZNmjSx/ly9evUS7RY98q5WrVoXjWPUqFG677779MYbb2jevHkaOnSoateuXWb9xo0bW79UuFCzZs1K7Me6desqLS3NpuzFF1/U999/r02bNpV6m8Dp06dtthEAAG9B0g8AgJ+75ppr9MUXX6h169aqVq3kr/7LL79c1atX18aNG9WqVStJ0pkzZ7R3716bK+5NmjTRsWPHrK/37dtnc3X7mmuu0aJFi9S0aVPVq1fPqVg7deqkwsJC/fzzzyWuwBe55ZZbFBwcrFmzZmn58uVas2ZNuW127txZs2bNkmEY1tsOHPHFF1/o+eef17Jly3T55ZeXWF80kqFz584Otw0AgLsxkR8AAH4uLi5Op0+f1j/+8Q8lJCTowIED+v777zVixAhZLBbVqVNHDzzwgJ566in9+OOP2rFjh4YPH15iCPuNN96omTNn6rffftPmzZv1yCOP2Fy1v+eee9S4cWPdcccdWrt2rZKTk7V69Wo99thj+uuvv+yKtXXr1oqNjdXIkSO1dOlSaxuLFy+21gkMDNTw4cM1YcIEXXHFFerevXu5bfbu3VtZWVnauXOnA3vtvB07duj+++/XM888ow4dOiglJUUpKSk6ffq0tc6vv/6qoKCgi8YBAIAnkPQDAODnWrRooV9++UUWi0V9+/ZVp06dNG7cONWvX9+a2E+bNk09evTQgAEDFBMTo+uvv77E3ACvvfaawsLC1KNHD91999168sknbYbV165dW2vWrFGrVq00ePBgtW/fXg888IByc3MduvI/a9Ys3XnnnRo9erSuuuoqPfjgg8rOzrap88ADDyg/P18jRoy4aHuNGjXSoEGDSr3t4GI2b96ss2fP6sUXX1Tz5s2ty+DBg611PvnkE91zzz3l3mIAAICnmIwLb84DAACQ1KtXL0VGRmr69OmeDqWEtWvX6qabbtLhw4cVGhp60frbt29Xnz59dODAgVInNHTWyZMn1a5dO23evFlt2rRxWbsAALgKV/oBAIDPyMvL019//aXJkyfrrrvusivhl6Srr75aL7/8spKTk10az8GDB/X222+T8AMAvBYT+QEAAJ/xySef6IEHHlBkZKQ+/PBDh947fPhwl8fTtWtXde3a1eXtAgDgKgzvBwAAAADATzG8HwAAAAAAP0XSDwAAAACAnyLpBwAAAADAT5H0AwAAAADgp0j6AQAAAADwUyT9AAAAAAD4KZJ+AAAAAAD8FEk/AAAAAAB+iqQfAAAAAAA/9f8A/dmaWZyS6dsAAAAASUVORK5CYII=", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -306,34 +372,32 @@ } ], "source": [ - "plt.figure(figsize=(12, 5))\n", - "delta_psi_tidal = ph_tdLAL - tidal_phase_corr\n", - "plt.plot(fs, abs(delta_psi_tidal), \"-\", color=\"black\", label='')\n", - "plt.axvline(f_merger_ripple, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger_ripple, linestyle=\"--\", color=\"black\")\n", - "# plt.legend()\n", - "plt.title(\n", - " r\"Tidal phase difference ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5, 1.2 * f_merger_ripple + 20)\n", - "plt.yscale(\"log\")\n", + "plt.plot(fs, ph_tdLAL, label='LAL', lw=2.5)\n", + "plt.plot(fs, tidal_phase_corr, label='ripple no min', ls='--')\n", + "plt.plot(fs, tidal_phase_corr_withMIN, label='ripple with min', ls='--')\n", + "# plt.axvline(fs[first_valid], min(ph_tdLAL), max(ph_tdLAL), c='r', ls='-.', alpha=0.7)S\n", + "plt.legend()\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "da761f90", + "metadata": {}, + "source": [ + "### NRTidal phase component\n", + "(not spin!)" + ] + }, { "cell_type": "code", - "execution_count": 77, - "id": "8af9d8b5", + "execution_count": 56, + "id": "fcfeb8b7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -344,58 +408,76 @@ ], "source": [ "plt.figure(figsize=(12, 5))\n", - "plt.plot(fs, jnp.cumsum(delta_psi_tidal), c='k')\n", - "plt.axvline(f_merger_ripple, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger_ripple, linestyle=\"--\", color=\"black\")\n", + "delta_psi_tidal = ph_tdLAL - tidal_phase_corr\n", + "delta_psi_tidal_withMIN = ph_tdLAL - tidal_phase_corr_withMIN\n", + "plt.plot(fs, abs(delta_psi_tidal), \"-\", color=\"black\", label='No min')\n", + "plt.plot(fs, abs(delta_psi_tidal_withMIN), \"-\", color=\"orange\", alpha=0.6, label='With min')\n", + "plt.axvline(1.15 * f_merger_ripple, linestyle=\"--\", color=\"r\", label=\"Planck taper\")\n", + "plt.axvline(1.35 * f_merger_ripple, linestyle=\"--\", color=\"r\")\n", + "plt.legend()\n", "plt.title(\n", - " r\"Cumulative error ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", + " r\"Tidal phase difference ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", " m1, m2, chi1, chi2, lambda1, lambda2\n", " )\n", ")\n", "plt.xlabel(\"Frequency (Hz)\")\n", + "plt.ylabel(r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", "plt.xscale(\"log\")\n", "plt.xlim(f_l - 5, 1.2 * f_merger_ripple + 20)\n", - "# plt.yscale(\"log\")\n", + "plt.yscale(\"log\")\n", "plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "080c2e51", + "cell_type": "markdown", + "id": "51a87712", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### $\\kappa^{2T}$ check" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "b4da0afb", + "execution_count": 57, + "id": "6a2ca110", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "187.8375\n", + "187.8375\n" + ] + } + ], + "source": [ + "kappa_LAL = lalsim.SimNRTunedTidesComputeKappa2T(m1_kg, m2_kg, lambda1, lambda2)\n", + "print(kappa_LAL)\n", + "print(kappa)" + ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 59, "id": "bd08c356", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Array([ 5.4772518e-24-2.8787021e-24j, 1.0637312e-24+6.0926741e-24j,\n", - " -6.1303162e-24-7.9777775e-25j, ..., 0.0000000e+00+0.0000000e+00j,\n", - " 0.0000000e+00+0.0000000e+00j, 0.0000000e+00+0.0000000e+00j], dtype=complex64)" + "Array([ 5.5163288e-24-2.8030924e-24j, 9.7971486e-25+6.1067470e-24j,\n", + " -6.1187298e-24-8.8225277e-25j, ..., -0.0000000e+00+0.0000000e+00j,\n", + " -0.0000000e+00+0.0000000e+00j, -0.0000000e+00+0.0000000e+00j], dtype=complex64)" ] }, - "execution_count": 78, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# And finally lets generate the waveform!\n", + "# Ripple waveform generation\n", "hp_ripple, hc_ripple = gen_IMRPhenomXAS_NRTidalv3_hphc(\n", " fs_ripple, theta_ripple, f_ref\n", ")\n", @@ -404,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 60, "id": "5616c838", "metadata": {}, "outputs": [], @@ -466,15 +548,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "60d5b5e6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 80, + "execution_count": 61, "id": "05c1e340", "metadata": {}, "outputs": [], @@ -484,13 +558,13 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 62, "id": "0f7b849c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -519,15 +593,23 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "b1a7a0d9", + "metadata": {}, + "source": [ + "## Check total amplitude and phase" + ] + }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 63, "id": "5940b64e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -607,137 +689,13 @@ }, { "cell_type": "code", - "execution_count": 83, - "id": "7146f064", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", - "plt.subplot(2, 1, 1)\n", - "\n", - "# Plot the amplitude\n", - "diffs_amplitude = abs(A_lalsuite - A_ripple) / A_lalsuite\n", - "plt.plot(f, diffs_amplitude, \"-\", color=\"black\")\n", - "\n", - "# Plot where the Planck taper has to take place\n", - "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", - "\n", - "plt.title(\n", - " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.yscale(\"log\")\n", - "plt.xscale(\"log\")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(\"Amplitude\")\n", - "# plt.ylim(1e-27, 1e-21)\n", - "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", - "# plt.legend()\n", - "\n", - "# Plot the angle or the phase\n", - "plt.subplot(2, 1, 2)\n", - "\n", - "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\")\n", - "name = \"Angle\"\n", - "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", - "# plt.legend()\n", - "plt.title(\n", - " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " name, m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(f\"{name}\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "bc30bd23", - "metadata": {}, - "outputs": [], - "source": [ - "A_lalsuiteXAS = jnp.abs(hpXAS_lalsuite)\n", - "angle_lalsuiteXAS = np.unwrap(np.angle(hpXAS_lalsuite))\n", - "phase_lalsuiteXAS = hpXAS_lalsuite / A_lalsuiteXAS\n", - "\n", - "spin_corr_LAL = angle_lalsuite - angle_lalsuiteXAS - ph_tdLAL\n", - "spin_corr_ripple = get_spin_phase_correction(M_omega**(2/3), theta_intrinsic)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "a24ff196", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -749,7 +707,6 @@ "source": [ "plt.figure(figsize=(12, 5))\n", "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\", label='Total relative error')\n", - "plt.plot(f, abs((spin_corr_LAL - spin_corr_ripple)/spin_corr_LAL), label=r\"$|\\Delta \\psi_S / \\psi_S^{LAL}|$\")\n", "plt.plot(fs, abs(delta_psi_tidal/ph_tdLAL), \"-\", label=r\"$|\\Delta \\psi^{NRT3a}_T / \\psi_T^{LAL}|$\")\n", "plt.legend()\n", "plt.title(\n", @@ -768,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 22, "id": "da2b0f0f", "metadata": {}, "outputs": [], @@ -789,15 +746,16 @@ }, { "cell_type": "markdown", - "id": "f4f57c6e", + "id": "d5be7ab4", "metadata": {}, "source": [ - "### Test double counting of $\\psi^{NRT3a}_T$ and $\\psi_{spin}$" + "## Phase Checks\n", + "Let's extract the BBH phase after it has been aligned with the tidal corrections, but no global tidal corrections have been made. So only the alignment has been corrected. This is something we can extract from LAL" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 66, "id": "2d659e2e", "metadata": {}, "outputs": [], @@ -814,7 +772,7 @@ "lalsim.SimInspiralWaveformParamsInsertdQuadMon1(laldict_OnlyPhase, quad1 - 1)\n", "lalsim.SimInspiralWaveformParamsInsertdQuadMon2(laldict_OnlyPhase, quad2 - 1)\n", "\n", - "# Specify we only want phase contribution (before LAL seems to add tidal ans spin phase terms again)\n", + "# Specify we only want phase contribution (before LAL adds tidal and spin phase terms)\n", "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", "\n", "phaseLAL = lalsim.SimIMRPhenomXASGenerateFD(\n", @@ -852,47 +810,60 @@ }, { "cell_type": "code", - "execution_count": 89, - "id": "bcd5c320", + "execution_count": 67, + "id": "1bc242c1", "metadata": {}, "outputs": [], "source": [ - "A_h22 = jnp.abs(h22)\n", - "angle_h22 = np.unwrap(np.angle(h22))\n", - "phase_h22 = h22 / A_h22" + "psi_bbh, phase_shift, phifRef, phiTfRef, dphi_merger, psi_T, psi_SS = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" ] }, { "cell_type": "code", - "execution_count": 90, - "id": "92c27e7b", + "execution_count": 68, + "id": "a7ae5689", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[8.95752162e+00+0.j 1.71211590e+01+0.j 2.52763224e+01+0.j ...\n", - " 1.25424578e+04+0.j 1.25424578e+04+0.j 1.25424578e+04+0.j]\n", - "[2.67433632e+00 4.55478840e+00 6.42676652e+00 ... 5.58697168e+03\n", - " 5.58697168e+03 5.58697168e+03]\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "print(phaseLAL)\n", - "print(angle_h22)" + "plt.plot(f, phaseLAL.real, label=r'$\\psi_{tot}^{LAL}$')\n", + "plt.plot(f, psi_bbh + phase_shift, ls='--', label=r'$\\psi_{BBH} + \\psi_{fref}$')\n", + "# plt.plot(f, phase_shift, ls='--', label=r'$\\psi_{fref}$')\n", + "plt.plot(f, psi_T, ls='--', label=r'$\\psi_T$')\n", + "plt.plot(f, psi_SS, ls='--', label=r'$\\psi_S$')\n", + "plt.legend()\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6485586b", + "metadata": {}, + "source": [ + "$\\psi_T$ won't play a role here as this is not extracted from LAL. Let's look further into the difference between the tidal-aligned BBH baselines" ] }, { "cell_type": "code", - "execution_count": 91, - "id": "09d0b96c", + "execution_count": 69, + "id": "37a2cbc6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -902,40 +873,154 @@ } ], "source": [ - "plt.plot(f, phaseLAL.real)\n", - "plt.plot(f, angle_h22.real)\n", - "plt.xscale('log')\n", + "plt.plot(f, (phaseLAL.real - (psi_bbh + phase_shift)))\n", + "# plt.xscale('log')\n", "# plt.yscale('log')\n", + "plt.title(r'NRTidalv3-aligned $\\Delta \\psi_{BBH}^{XAS}$')\n", + "plt.ylabel(r'$\\Delta \\psi$')\n", + "plt.xlabel('f (Hz)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2d8f39ce", + "metadata": {}, + "source": [ + "This error increases linearly with frequency. The only term that scales linearly with frequency in the tidal-aligned baseline is linb*Mf (line 840 in LALSimIMRPhenomX.c). So this is probably where the alignment error lies." + ] + }, + { + "cell_type": "markdown", + "id": "8cf262e7", + "metadata": {}, + "source": [ + "Subtract XAS (non-aligned) baseline from LAL XAS-NRTidalv3-aligned to get phase offsets" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e6521469", + "metadata": {}, + "outputs": [], + "source": [ + "from ripplegw.waveforms.IMRPhenomXAS import Phase\n", + "from ripplegw.waveforms.IMRPhenomX_utils import PhenomX_phase_coeff_table as bbh_phase_coeffs\n", + "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import fullTidalPhaseCorrection" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "d0a925a3", + "metadata": {}, + "outputs": [], + "source": [ + "ripple_XAS_phase = Phase(f, jnp.array([m1,m2,chi1,chi2]), bbh_phase_coeffs)\n", + "LAL_phase_offset = phaseLAL.real - ripple_XAS_phase # Should consist of linb*Mf + lina(=0) + phifRef\n", + "LAL_linbMf = LAL_phase_offset + phifRef # will probably contain a slight error due to dphi_merger*fref" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "586a19ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f, LAL_linbMf)\n", + "plt.plot(f, -dphi_merger*(f*M_s))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "6c960ccb", + "id": "3cf626db", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 92, - "id": "1bc242c1", + "execution_count": 38, + "id": "89afcebc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f, (phaseLAL.real - (psi_bbh + phase_shift)) - LAL_linbMf) # difference appears to be linbMf?\n", + "plt.yscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e95eb1f8", "metadata": {}, "outputs": [], "source": [ - "angle_rp = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" + "dphiXAS = jax.grad(Phase)(f_merger, theta_intrinsic[:4], bbh_phase_coeffs)\n", + "dphiNRTidal = jax.grad(fullTidalPhaseCorrection)(f_merger, theta_intrinsic, f_merger, no_taper=False)" ] }, { "cell_type": "code", - "execution_count": 93, - "id": "37a2cbc6", + "execution_count": 40, + "id": "d7c866b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0020110738\n", + "-0.036770605\n", + "-0.03878169\n", + "[3.9062500e-03 2.9296875e-03 1.9531250e-03 ... 1.6522461e+01 1.6522461e+01\n", + " 1.6522461e+01]\n" + ] + } + ], + "source": [ + "print(dphiXAS)\n", + "print(dphiNRTidal)\n", + "print(dphi_merger)\n", + "print(LAL_linbMf)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "0db64257", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -945,11 +1030,19 @@ } ], "source": [ - "plt.plot(f, (phaseLAL.real - (angle_rp - tidal_phase_corr + PI))/phaseLAL.real)\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", + "plt.plot(f, fullTidalPhaseCorrection(f, theta_intrinsic, f_merger, no_taper=False))\n", + "plt.plot(f, dphiNRTidal*(f-f_merger) + fullTidalPhaseCorrection(f_merger, theta_intrinsic, f_merger, no_taper=False))\n", + "plt.scatter(f_merger, fullTidalPhaseCorrection(f_merger, theta_intrinsic, f_merger, no_taper=False))\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99a3e0eb", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 01e133c53b173a0f0219d8363b6e24f6b2b04d09 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Wed, 5 Nov 2025 16:56:49 +0100 Subject: [PATCH 029/474] Implemented NRTidalv3 for XAS --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 18 +- test/NRTidalv3_testing.ipynb | 288 ++++++++++-------- test/benchmark_waveform.py | 39 ++- 3 files changed, 196 insertions(+), 149 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index 34e66c42..1cb77205 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -79,8 +79,11 @@ def _gen_IMRPhenomXAS_NRTidalv3( # Tidal phase offset # f_final = f[-1] - if f_merger < f_final: - f_final = f_merger + f_final = jax.lax.select( + f_merger < f_final, + f_merger, + f_final + ) bbh_phase_coeffs = IMRPhenomX_utils.PhenomX_phase_coeff_table # Note: the π shift from Y22 has been moved to the calculation of h0 @@ -88,10 +91,12 @@ def _gen_IMRPhenomXAS_NRTidalv3( Phase(f_ref, theta_intrinsic[:4], bbh_phase_coeffs) - PI / 4.0 ) # This is part of the BBH phase alignment - phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, f_final, no_taper) # This is part of the tidal correction to the phase alignment + phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, f_merger, no_taper) # This is part of the tidal correction to the phase alignment + + dphiXAS = jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs) + dphiT = jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, f_merger, no_taper) + dphi_merger = -(dphiXAS - dphiT) / M_s # linb from LAL - dphi_merger = -jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs)\ - + jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, f_final, no_taper) ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] phase_shift = -(phifRef - phiTfRef + dphi_merger*(f_ref*M_s)) + dphi_merger*f_Ms + ext_phase_contrib @@ -113,6 +118,7 @@ def _gen_IMRPhenomXAS_NRTidalv3( # Redefine planck taper as LAL uses the merger frequency for this computation, not f_final (which can be f_merger, but isn't guaranteed) + # Ok, this^ is not correct, planck taper can be passed as argument to fullTidalPhaseCorrection if no_taper: P_P = jnp.ones_like(f) A_P = jnp.ones_like(f) @@ -124,7 +130,7 @@ def _gen_IMRPhenomXAS_NRTidalv3( psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) if get_phase: # purely for debugging purposes - return bbh_psi, phase_shift, phifRef, phiTfRef, dphi_merger, psi_T, psi_SS + return bbh_psi, phase_shift, phifRef, phiTfRef, dphiXAS, dphiT, dphi_merger, psi_T, psi_SS # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) h0 = A_P * (bbh_amp + A_T) * jnp.exp(1.0j * ((bbh_psi + phase_shift + PI) - (psi_T + psi_SS))) # The additional π shift comes from Y22 diff --git a/test/NRTidalv3_testing.ipynb b/test/NRTidalv3_testing.ipynb index 8e232edc..6280d1f5 100644 --- a/test/NRTidalv3_testing.ipynb +++ b/test/NRTidalv3_testing.ipynb @@ -80,8 +80,8 @@ "source": [ "m1 = 1.37 # In solar masses\n", "m2 = 1.37\n", - "chi1 = 0 #0.141 # Dimensionless spin\n", - "chi2 = 0 #0.141\n", + "chi1 = 0.141 # Dimensionless spin\n", + "chi2 = 0.141\n", "lambda1 = 1001.8\n", "lambda2 = 1001.8\n", "tc = 0.0 # Time of coalescence in seconds\n", @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "id": "a65d3d67", "metadata": {}, "outputs": [ @@ -151,8 +151,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "1595.8424\n", - "1595.8423419624833\n" + "1623.969\n", + "1623.9690632395718\n" ] } ], @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 6, "id": "c1dbdc6b", "metadata": {}, "outputs": [], @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 7, "id": "610906ab", "metadata": {}, "outputs": [ @@ -241,13 +241,13 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 8, "id": "f842e550", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -268,13 +268,13 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 9, "id": "9d8657b1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 10, "id": "d943c569", "metadata": {}, "outputs": [ @@ -306,7 +306,7 @@ "0" ] }, - "execution_count": 54, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -356,13 +356,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "id": "c3339856", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -391,13 +391,13 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 12, "id": "fcfeb8b7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+qMfemfZK0zngKEePryvPqfx8+eWXevLJJ3Xo0CHVrVtXknTvvffKarVq7Nixeuihh9zyOenM9pXUfeGqz1uUDCT9gIs8++yzmjdvnkaNGqWOHTuqUqVKMplMevDBB2W1Wl3WTl4fvnn1HFyZ2Dsa4wMPPGDrmV65cqUmTZqkd955R99++6169+5tK/vII49o0KBBubZ75SgETylMnFf/cS3stjpyPPLjin1crVo1ZWZmKiEhQRUqVLCt3717t2rXrp1jFMeuXbvUrFmzHD0Ou3btsmurdu3aGj9+vL744guHtiU3RYkhMzNTYWFh+u2331S3bl0tXrxYffv21bFjx3L0UF28eFGBgYF5/tOUnp6u2NhYh2KuUaNGrsc1r/3sLvfdd5+eeuopHTp0SI0bN87x+pIlS5ScnKyHH37Y7bE4I7+4HY25oOOZraBjkv2F25QpU3TXXXfpkUcesXvdFed4Qe2UpvO4du3aOn36dI73nD17VpJyHRGWF2frKuox/+WXX3T27Fm9+eabduXvvvtuBQYGasGCBXZJf1GPvTPtlaZzwFGOHl9XnlP5+fDDD9WqVStbkpvt7rvv1vz587Vz5063fFnizPaV1H3h6O8eSgeSfiAPNWrUUPny5XX48OEcrx08eDDHuq+//lqDBg3S5MmTbetSU1MdmmU7P4cPH7brfT5y5IisVmuhJudyJsbatWtr+PDhGj58uM6fP6/WrVvrrbfeUu/evVWjRg1VqFBBFovF6T+Wzu7XoihKnK6sI7c6K1asqD179ri13exe1qioKLukfffu3WrZsmWO8rt27dKdd95pty4jI0MHDx60G3Ldr18/SSrSqIyixBAUFKRXX33V9vqDDz6o0aNH6+DBg2rTpo3de6OionTjjTfmGcfGjRvVrVs3h2KOiorK9fcur/3sLtlDPfMaQbRgwQIFBwfnGEXhafnF7WjMBR3PbAUdk+wvHSpXrpzrnUNccY4X1E5pOo/Dw8O1Zs0au8vFJGnz5s221x3lbF1FPeYLFiyQn5+f+vfvb1c+KChId955p7777jvNnDnTltgU9dg7015pOgcc5ejxdeU5lZ9z587ZRtpdKSMjQ1LWFy/u4Mz2ldR94ejvHkoHbtkH5MFsNqtnz55asmSJTpw4YVu/f/9+/fzzz7mWv7oX93//+58sFkuR4pg+fXqOOiWpd+/eTtflSIwWiyXHP+U1a9ZUnTp1bMPPzGaz/vGPf+ibb77JNXGNiYnJNwZn9mtRFCVOV9ZxNR8fH/Xr109Lly7Vtm3bcrxuGIZL2s0eInplGxaLRfv27cuRcF+4cEFnz57NsX7//v3KyMhwaTLr6hgOHz6s2NhYNWzYMMdrO3bs0M0335xnLNnXwTryyOs62Nz2s7OSk5N14MABu7kbcrt8IyMjQ59++qnKly+vJk2a5Hg9JiZGq1evVv/+/d1+DWZuMUvOx+1MzAUdz2wFHZOPPvpIUlYPl6t60YraTkk+j++77z5ZLBa7S43S0tI0b948dejQwamZxZ2tqyjHPCUlRd9++63uuOOOXJOdAQMGKCEhQT/88IPD8eenqO2V5HPAUY4eX1eeU1e6+nOpUaNG2rlzpw4dOmRXbuHChfLx8XHbF7XObJ879kVun8/O7gtHf/dQOtDTD+Tjtdde04oVK9SpUycNHz5cmZmZ+t///qemTZvaJiLLdtddd+mzzz5TpUqV1KRJE23atEmrV68u8rViUVFRuvvuu9WrVy9t2rRJn3/+uQYOHJhrL2lBHIkxISFBdevW1X333aeWLVsqODhYq1ev1tatW+1GCLz99ttas2aNOnTooCeeeEJNmjRRbGysduzYodWrV+c7zNCZ/VpURYnTlXVcbcKECVq5cqW6dOmiJ598UjfeeKPOnj2rr776Sr/99psqV65c5HavvfZaNWvWTKtXr9bQoUMlZf1TmZqamuP8yb4V3tXrs4+Hs/8YmUwmdenSRWvXrs3xmitjSElJ0SOPPKKXX35ZlSpVsntt+/btio2N1T333JNnnK64Dja3/Zxt2rRpiouL05kzZyRJS5cu1alTpyRlXW6THfOWLVvUrVs3jRs3znYN8VNPPaX4+Hh17txZ11xzjaKjo7VgwQIdOHBAkydPznWyrUWLFikzMzPfYfL5HRtn4s4t5sLE7UjMkmPHM1t+x+To0aP6v//7P0nSgQMHCqwrLwXtR2faKenncYcOHXT//ffr5Zdf1vnz59WwYUN98sknOnbsmD7++GO7sgXtF2fqKuox/+GHH5SQkJDnrSLvvPNOVahQQQsWLCjwNpiObGNR2ivp54Cjn2WOHl9nzgNn2r/6c+nFF1/U8uXL1alTJ40cOVLVqlXTjz/+qOXLl+vxxx/P8WVcQeevq/eDs2WL8jfFmX3hzO8eSolivVcAUAI4c8s+wzCMX3/91WjTpo3h7+9vXHvttcbMmTNtt2W70sWLF40hQ4YY1atXN4KDg42ePXsaBw4cMOrXr28MGjQo3zZyk93Gvn37jPvuu8+oUKGCUaVKFWPkyJFGSkpKjnIxMTEFbosjMaalpRkvvvii0bJlS6NChQpGUFCQ0bJly1xvo3Xu3DljxIgRRmhoqOHn52eEhIQYt912mzF79ux8t82Z/VrUW/Y5Gmde+9HZbXXmeBw/ftx47LHHjBo1ahgBAQHGtddea4wYMcJIS0tzut28TJkyxQgODrbd3m/x4sWGJGPPnj05ykky4uLi7Nb/61//MipWrGi7jeCVnnrqKWPcuHE51ickJBiSjAcffDDXmFwVQ3p6unHnnXcaAwcOzDW+sWPHGvXq1cv1NVe7ej9nq1+/vkO3zco+f6/cnwsXLjR69Ohh1KpVy/D19TWqVKli9OjRw/j+++/zjOOmm24yatasaWRmZub6ekHHxpm4c4u5MHEXFHM2Z49nbsfEarUaXbp0MapUqWIMGTLECA4Ozre+wp7jzrRTGs5jwzCMlJQUY8yYMUZISIgREBBgtGvXzlixYoVdGUfPL0fqMoyiH/O+ffsaAQEBdrf/vNrDDz9s+Pn55bg1amGOfWHbKw3ngKOfZYbh+PF1tJwz7ef2ubR582ajd+/eRkhIiOHn52c0atTIeOutt4yMjAy7Nhw5f92xH5wpW5T94My+KM5zDsWDpB8ooQpKQgFHxMXFGVWrVjXmzJnj8rrz+qd42bJlhslkMnbv3u3yNrNZLBZjwIABxl133ZXjnxXDMIzU1FQjJCTEmDp1qttiuJI797MrFcexcYfCHM/cjsm0adMMScann35qLFy40JBkHD16NM86CnuOO9qOt53Hrjy/XHXMC6u4Pt+87RwozUrr56OrFfc5h+LBNf0A4MUqVaqkf/3rX5o0aZLL7iKRmZmp1NRUWSwWu+Vsa9as0YMPPqjmzZu7pL3cPPXUU7bLIXx9c16pNm/ePPn5+enpp592WwxXcsd+dofiODbuUJjjefUxOXbsmF566SX17dtXjz76qG0f7NixI8d7i3KOO9OOt53Hrjy/XHHMC6O4P9+87RwozUrr56OrFfc5h2Li6W8dAOSOnn6UVNnn5pWPefPmFVv7x44dMyQZ5cqVM4KCgmyPdevWFVsMKF2sVqvRvXt3o0qVKsaZM2cMwzCMjIwMIzg42GjUqJExa9YsIzEx0Va+sOe4M+1wHpdMxfn5xjkAoLiYDMPBm0YDKFbjx4/Xa6+9ppiYGFWvXt3T4QBAqTVr1iw9/fTT+vTTT/Xoo4/a1s+fP1//+c9/FBMTo4SEBPn5+ZWKdgAAcAZJPwAAAAAAXopr+gEAAAAA8FIk/QAAAAAAeKmc04TCKVarVWfOnFGFChVkMpk8HQ4AAAAAwMsZhqGEhATVqVNHPj759+WT9BfRmTNnFBoa6ukwAAAAAABlzMmTJ1W3bt18y5D0F1GFChUkZe3sihUrejgaAAAAAECpZLVKJ09mLYeGSvn04MfHxys0NNSWj+aHpL+Isof0V6xYkaQfAAAAAFA4SUlSixZZy4mJUlBQgW9x5BJzJvIDAAAAAMBLkfQDAAAAAOClSPoBAAAAAPBSXNNfTCwWizIyMjwdBkoYf3//Am+xAQAAAACFRdLvZoZhKDo6WnFxcZ4OBSWQj4+PGjRoIH9/f0+HAgAAAMALkfS7WXbCX7NmTQUGBjo0uyLKBqvVqjNnzujs2bOqV68e5wYAAAAAlyPpdyOLxWJL+KtVq+bpcFAC1ahRQ2fOnFFmZqb8/Pw8HQ4AAAAAT/H1lYYP/3vZVdW6rCbkkH0Nf2BgoIcjQUmVPazfYrGQ9AMAAABlWUCANH26y6tlBrFiwLBt5IVzAwAAAIA70dMPAAAAAICnGYZ04ULWcvXqkos6COnpR6lhMpm0ZMkST4cBAAAAAK6XnCzVrJn1SE52WbUk/cjV4MGDZTKZ9Pbbb9utX7JkiceGpJ89e1a9e/f2SNsAAAAAUBqR9CNP5cqV0zvvvKOLFy96OhRJUkhIiAICAjwdBgAAAACUGiT9yFOPHj0UEhKiiRMn5lvum2++UdOmTRUQEKCwsDBNnjw53/Ljx49XeHi45s6dq3r16ik4OFjDhw+XxWLRu+++q5CQENWsWVNvvfWW3fuuHN5/7NgxmUwmffvtt+rWrZsCAwPVsmVLbdq0qUjbDAAAAAAlkmFIicekmI3Shc0Ov42J/IqZYRhKduH1Gc4IDAx0ami+2WzWhAkTNHDgQD333HOqW7dujjLbt2/XAw88oPHjx2vAgAHauHGjhg8frmrVqmnw4MF51n306FEtX75cK1as0NGjR3Xffffpzz//VKNGjfTrr79q48aNGjp0qHr06KEOHTrkWc8rr7yi9957T9dff71eeeUVPfTQQzpy5Ih8XXhfSwAAAADwGMOQ/toinf5BSrs80V9iusNvJzMqZsnJyQoODvZI24mJiQoKCnLqPf3791d4eLjGjRunjz/+OMfrU6ZM0W233ab//Oc/kqRGjRpp3759mjRpUr5Jv9Vq1dy5c1WhQgU1adJE3bp108GDB/XTTz/Jx8dHjRs31jvvvKM1a9bkm/SPGTNGd955pyTptddeU9OmTXXkyBHdcMMNTm0nAAAAAJQ4mcnSn/OkuN1Zz33KScENJJ80h6tgeD8K9M477+iTTz7R/v37c7y2f/9+3XLLLXbrbrnlFh0+fFgWiyXPOsPCwlShQgXb81q1aqlJkyby8fGxW3f+/Pl8Y2vRooVtuXbt2pJU4HsAAAAAoMRLOSftnZiV8Jt8pbr9pFaTpBtGSY2ecbgaevqLWWBgoBITEz3WdmF07txZPXv21Msvv5xv770z/Pz87J6bTKZc11mtVofryb50oaD3AAAAAECJ4+srDRqUtZweLUXNkDITJP9q0vVPSUH1C1etC0OEA0wmk9ND7EuCt99+W+Hh4WrcuLHd+htvvFEbNmywW7dhwwY1atRIZrO5OEMEAAAAgNIrIECaP19KjJIOfiBZkqXAUKnxPyW/CgW+PS8k/XBI8+bN9fDDD+uDDz6wW//CCy+oXbt2euONNzRgwABt2rRJ06ZN04cffuihSAEAAACglEo+83fCH3yd1Gik5Fu4EdvZuKYfDnv99ddzDJ1v3bq1Fi9erC+//FLNmjXTq6++qtdff91llwEAAAAAQJmQekGKnCQlXpKCGmT18Bcx4Zckk2EYhgvCK7Pi4+NVqVIlXbp0SRUrVrR7LTU1VVFRUWrQoIHKlSvnoQhRknGOAAAAAJAlXdr2hnTTm1nP485JlWrmWTy/PPRq9PQDAAAAAOAphiEd+1xKPf33Ol/XzQNH0g8AAAAAgKfErJf+2ix3peck/QAAAAAAeELqBenEV1nLde92SxMk/QAAAAAAFDfDkKI+kazpUoVGUq3ubmmGpB8AAAAAgOJ2YZOUcEjy8ZcaDJJMJrc0Q9IPAAAAAEBxsqRJp5ZkLV/TVypX3W1N+bqtZgAAAAAAkNPZlVLGJSmg+t/D+s1m6b77/l52EZJ+AAAAAACKS2ayFL06azn0Xsnnclperpz01Vcub47h/QAAAAAAFJfzv0rWVKl8HalKa7c3R9IPl1m7dq1MJpPi4uLyLRcWFqapU6eWmHgAAAAAoFhYM6ToiKzl2r3cNnnflUj6kcPMmTNVoUIFZWZm2tYlJibKz89PXbt2tSubnVgfPXpUN998s86ePatKlSpJkubPn6/KlSsXY+T2ro4HAAAAADwqdqeUmSD5V5GqtrV/LSkp60sAkylr2UVI+pFDt27dlJiYqG3bttnWrV+/XiEhIdq8ebNSU1Nt69esWaN69erpuuuuk7+/v0JCQmQqhm+rHFHS4gEAAABQxl3YkPWz+i2Sj+sm68sPST9yaNy4sWrXrq21a9fa1q1du1b33HOPGjRooN9//91ufbdu3WzL2cPp165dqyFDhujSpUsymUwymUwaP3687X3JyckaOnSoKlSooHr16mn27Nn5xtS1a1c9++yzGjVqlKpUqaJatWrpo48+UlJSkoYMGaIKFSqoYcOGWr58uV1sVw7vzx558PPPP+vGG29UcHCwevXqpbNnzxZ9pwEAAABAflIvSPEHJJmkGjcXW7Mk/cXNMLLuyeiJh2E4HGa3bt20Zs0a2/M1a9aoa9eu6tKli219SkqKNm/ebEv6r3TzzTdr6tSpqlixos6ePauzZ89qzJgxttcnT56stm3baufOnRo+fLieeeYZHTx4MN+YPvnkE1WvXl1btmzRs88+q2eeeUb333+/br75Zu3YsUN33HGHHn30USUnJ+dZR3Jyst577z199tlnWrdunU6cOGEXFwAAAAC4RezWrJ8VG0sB1YqtWW7ZV9ys6dL25zzTdpsPJHOAQ0W7deumUaNGKTMzUykpKdq5c6e6dOmijIwMzZw5U5K0adMmpaWl5Zr0+/v7q1KlSjKZTAoJCcnxep8+fTR8+HBJ0tixY/X+++9rzZo1aty4cZ4xtWzZUv/+978lSS+//LLefvttVa9eXU888YQk6dVXX9WMGTO0e/du3XTTTbnWkR3/ddddJ0kaOXKkXn/9dYf2CQAAAAAU2sVdWT+rtinWZkn6kauuXbsqKSlJW7du1cWLF9WoUSPVqFFDXbp00ZAhQ5Samqq1a9fq2muvVb169Zyuv0WLFrbl7C8Gzp8/7/B7zGazqlWrpubNm9vW1apVS5LyrScwMNCW8EtS7dq1C2wXAAAAAIok/ZKUFJW1XLlF/mVdjKRf0o8//qgXXnhBVqtVY8eO1eOPP+6+xnz8s3rcPcHH3+GiDRs2VN26dbVmzRpdvHhRXbp0kSTVqVNHoaGh2rhxo9asWaPu3bsXKhQ/Pz+75yaTSVar1en3XLkue8K+/OrJrQ7DicseAAAAAMBpcbuzfgY1kPwrF2vTZT7pz8zM1OjRo7VmzRpVqlRJbdq0Uf/+/VWtmpuusTCZHB5i72ndunXT2rVrdfHiRb344ou29Z07d9by5cu1ZcsWPfPMM3m+39/fXxaLpThCBQAAAICSK/7y/GWVm+VdxmyW+vT5e9lFyvxEflu2bFHTpk11zTXXKDg4WL1799bKlSs9HVaJ0K1bN/3222+KjIy09fRLUpcuXTRr1iylp6fnej1/trCwMCUmJioiIkIXLlzId4I9AAAAAPBKhiElHM5artAo73LlyknLlmU9ypVzWfOlPulft26d+vbtqzp16shkMmnJkiU5ykyfPl1hYWEqV66cOnTooC1bttheO3PmjK655hrb82uuuUanT58ujtBLvG7duiklJUUNGza0XS8vZSX9CQkJtlv75eXmm2/W008/rQEDBqhGjRp69913iyNsAAAAACg50mKkjDjJ5CsFNyj25kv98P6kpCS1bNlSQ4cO1b333pvj9UWLFmn06NGaOXOmOnTooKlTp6pnz546ePCgatas6YGIS4+wsLBcr3evX79+ruu7du2aY/2MGTM0Y8YMu3XHjh3L8d7IyMh8Y1m7dm2OdbnVc2X7V8czePBgDR482K58v379uKYfAAAAgPtk9/IHhUk+fvkWdYdS39Pfu3dvvfnmm+rfv3+ur0+ZMkVPPPGEhgwZoiZNmmjmzJkKDAzU3LlzJWVNTHdlz/7p06dVp06dPNtLS0tTfHy83QMAAAAAgFwlncj6GXxtAeWSpKCgrEdSksuaL/VJf37S09O1fft29ejRw7bOx8dHPXr00KZNmyRJ7du31549e3T69GklJiZq+fLl6tmzZ551Tpw4UZUqVbI9QkND3b4dAAAAAIBSKvlk1s/Aug6UTc56uJBXJ/0XLlyQxWKxux5dyrqfe3R0tCTJ19dXkydPVrdu3RQeHq4XXngh35n7X375ZV26dMn2OHnypFu3AQAAAABQShmGlHwqaznQMx3Gpf6afle4++67dffddztUNiAgQAEBpeOWewAAAAAAD0q7IFnTsibxKx/ikRC8uqe/evXqMpvNOnfunN36c+fOKSTEMzscAAAAAFBGpJzJ+lm+tmTyTPrt1Um/v7+/2rRpo4iICNs6q9WqiIgIdezY0YORAQAAAAC8XlpM1s9ytfIv50alfnh/YmKijhw5YnseFRWlyMhIVa1aVfXq1dPo0aM1aNAgtW3bVu3bt9fUqVOVlJSkIUOGeDBqAAAAAIDXS72c9AfU8FgIpT7p37Ztm7p162Z7Pnr0aEnSoEGDNH/+fA0YMEAxMTF69dVXFR0drfDwcK1YsSLH5H4AAAAAALhUdk9/QPWCy/r4SF26/L3sIqU+6e/atasMw8i3zMiRIzVy5MhiiggAAAAAAGVN5CdJ5Rzo6S9fXlq71uUhePU1/XCf+fPnq3LlysXa5vjx4xUeHl6sbQIAAABAoRiGlPZX1rIjPf1uQtKPXA0ePFgmk0kmk0n+/v5q2LChXn/9dWVmZno6NKeYTCYtWbLE02EAAAAAKGsykyTjcv7kV8ljYZD0I0+9evXS2bNndfjwYb3wwgsaP368Jk2a5OmwSq309HRPhwAAAACguGTEZ/00B0k+DlxZn5Qk1aiR9UhKclkYJP3IU0BAgEJCQlS/fn0988wz6tGjh3744Ydcyx49elT33HOPatWqpeDgYLVr106rV6+2KxMWFqYJEyZo6NChqlChgurVq6fZs2fblTl16pQeeughVa1aVUFBQWrbtq02b96cZ5vXXnutRo4cmeu8DmFhYZKk/v37y2Qy2Z47Gusbb7yhhx56SEFBQbrmmms0ffp0uzJxcXF6/PHHVaNGDVWsWFHdu3fXrl27bK9nX44wZ84cNWjQQOXKlct1OwAAAAB4oYxLWT/9nejlv3Ah6+FCJP2ekpSU9yM11fGyKSmOlXWB8uXL59lbnZiYqD59+igiIkI7d+5Ur1691LdvX504ccKu3OTJk9W2bVvt3LlTw4cP1zPPPKODBw/a6ujSpYtOnz6tH374Qbt27dK//vUvWa3WHO3t3r1bt956qwYOHKhp06bJZDLlKLN161ZJ0rx583T27Fnbc0djnTRpklq2bKmdO3fqpZde0j//+U+tWrXK9vr999+v8+fPa/ny5dq+fbtat26t2267TbGxsbYyR44c0TfffKNvv/1WkZGRDuxlAAAAAF4hu6ffg0P7JS+Yvb/UCg7O+7U+faRly/5+XrOmlJyce9kuXexneAwLy/2boQLucJAfwzAUERGhn3/+Wc8++2yuZVq2bKmWLVvanr/xxhv67rvv9MMPP9jdOaFPnz4aPny4JGns2LF6//33tWbNGjVu3FhffPGFYmJitHXrVlWtWlWS1LBhwxxtbdy4UXfddZdeeeUVvfDCC3nGXaNG1gyZlStXVkhIiNOx3nLLLXrppZckSY0aNdKGDRv0/vvv6/bbb9dvv/2mLVu26Pz58woICJAkvffee1qyZIm+/vprPfnkk5KyhvR/+umntlgAAAAAlBHZPf1+FT0aBkk/8vTjjz8qODhYGRkZslqtGjhwoMaPH59r2cTERI0fP17Lli3T2bNnlZmZqZSUlBy95y1atLAtm0wmhYSE6Pz585KkyMhItWrVypbw5+bEiRO6/fbb9dZbb2nUqFGF2i5HY+3YsWOO51OnTpUk7dq1S4mJiapWrZpdmZSUFB09etT2vH79+iT8AAAAQFlk6+kn6S+bEhPzfs1stn9+OSnOlc9VV2gcO1bokK7WrVs3zZgxQ/7+/qpTp458ffM+XcaMGaNVq1bpvffeU8OGDVW+fHndd999OS4H8PPzs3tuMplsw/fLly9fYEw1atRQnTp1tHDhQg0dOlQVKzr/C+RorPlJTExU7dq1tTaX+2heeSvDoKAgp+MDAAAA4AUyL19m7ZvPKO9iQNLvKc4kg+4qW2BVQbkOr8/Nhg0bNHjwYPXv319SVlJ8zMkvIFq0aKE5c+YoNjY2z97+8uXL68cff1SfPn3Us2dPrVy5UhUqVMizTj8/P1kslkLF+vvvv+d4fuONN0qSWrdurejoaPn6+tomCAQAAAAAG8vl+dfMBXduuhMT+cElrr/+ettkdbt27dLAgQNznYAvPw899JBCQkLUr18/bdiwQX/++ae++eYbbdq0ya5cUFCQli1bJl9fX/Xu3VuJ+YyaCAsLU0REhKKjo3Xx4kWnYt2wYYPeffddHTp0SNOnT9dXX32lf/7zn5KkHj16qGPHjurXr59WrlypY8eOaePGjXrllVe0bds2p7YbAAAAgBdyNun38ZHats16XD2iuwhI+uESU6ZMUZUqVXTzzTerb9++6tmzp1q3bu1UHf7+/lq5cqVq1qypPn36qHnz5nr77bdlvvpyB0nBwcFavny5DMPQnXfeqaQ87lAwefJkrVq1SqGhoWrVqpVTsb7wwgvatm2bWrVqpTfffFNTpkxRz549JWVdlvDTTz+pc+fOGjJkiBo1aqQHH3xQx48fV61atZzabgAAAABeKDvp93Uw6S9fXtq6NevhwKXPjjIZud3gHA6Lj49XpUqVdOnSpRzXl6empioqKop7tJdCYWFhGjVqVKEnC3QU5wgAAADgpXb9R0o7L934olTBscumHZVfHno1evoBAAAAAHA1rukHAAAAAMBLOZv0JydLYWFZj+Rkl4XB7P1ALpy98wAAAAAA2FgzJCMza9nRpN8wpOPH/152EXr6AQAAAABwJWv638vmAM/FIZL+YsFcicgL5wYAAADghawZWT9NZsnk2bSbpN+N/Pz8JEnJLrweA94lPT3rG8DcbksIAAAAoJSyJf1+no1DXNPvVmazWZUrV9b58+clSYGBgTKZTB6OCiWF1WpVTEyMAgMD5evLryIAAADgNbKTfh+Sfq8XEhIiSbbEH7iSj4+P6tWrx5dBAAAAgDcxSPrLDJPJpNq1a6tmzZrKyMjwdDgoYfz9/eXjw1U2AAAAgFcpTE+/ySQ1afL3souQ9BcTs9nMddsAAAAAUBYU5pr+wEBp716Xh0IXIwAAAAAArmTr6fd8PztJPwAAAAAArmS7pt/fs3GIpB8AAAAAANcqzDX9yclS06ZZDxfe9t3zYw0AAAAAAPAmtmv6nUi5DUPat+/vZRehpx8AAAAAAFcyLFk/TZ6fzJ2kHwAAAAAAVzKsWT9J+gEAAAAA8DbZSb/nU27PRwAAAAAAgDfJ7umXyaNhSCT9AAAAAAC4Vgka3s/s/QAAAAAAuJJRiOH9JpNUv/7fyy5C0g8AAAAAgEsVYnh/YKB07JjLI2F4PwAAAAAArlSChveT9AMAAAAA4EqFGd7vJp6PAAAAAAAAr5Kd9DsxvD8lRWrXLuuRkuKySLimHwAAAAAAVyrM8H6rVdq27e9lF6GnHwAAAAAAV8pO+ktAyu35CAAAAAAA8CZGIYb3uwlJPwAAAAAArmRYsn4yez8AAAAAAN7GuPzT8ym35yMAAAAAAMCblKBb9jF7PwAAAAAArmQb3u9k0l+9ustDIekHAAAAAMClLg/vdybpDwqSYmJcHonnxxoAAAAAAOBVsq/pZ/Z+AAAAAADgJiT9AAAAAAB4WkqK1LVr1iMlxWXVck0/AAAAAACeZrVKv/7697KL0NMPAAAAAICXIukHAAAAAMBLkfQDAAAAAOClSPoBAAAAAPBSJP0AAAAAAHgpZu8HAAAAAKAkCAx0eZUk/QAAAAAAeFpQkJSU5PJqGd4PAAAAAIArGYanI7Ah6QcAAAAAwEuV+aT/5MmT6tq1q5o0aaIWLVroq6++8nRIAAAAAICyJjVVuvPOrEdqqsuqLfPX9Pv6+mrq1KkKDw9XdHS02rRpoz59+igoKMjToQEAAAAAygqLRfrpp7+XXaTMJ/21a9dW7dq1JUkhISGqXr26YmNjSfoBAAAAAKVeiR/ev27dOvXt21d16tSRyWTSkiVLcpSZPn26wsLCVK5cOXXo0EFbtmwpVFvbt2+XxWJRaGhoEaMGAAAAAMDzSnzSn5SUpJYtW2r69Om5vr5o0SKNHj1a48aN044dO9SyZUv17NlT58+ft5UJDw9Xs2bNcjzOnDljKxMbG6vHHntMs2fPdvs2AQAAAABQHEr88P7evXurd+/eeb4+ZcoUPfHEExoyZIgkaebMmVq2bJnmzp2rl156SZIUGRmZbxtpaWnq16+fXnrpJd18880Flk1LS7M9j4+Pd3BLAAAAAAAoXiW+pz8/6enp2r59u3r06GFb5+Pjox49emjTpk0O1WEYhgYPHqzu3bvr0UcfLbD8xIkTValSJduDSwEAAAAAACVVqU76L1y4IIvFolq1atmtr1WrlqKjox2qY8OGDVq0aJGWLFmi8PBwhYeH648//siz/Msvv6xLly7ZHidPnizSNgAAAAAA4C4lfni/u916662yWq0Olw8ICFBAQIAbIwIAAAAAlDlBQZJhuLzaUt3TX716dZnNZp07d85u/blz5xQSEuKhqAAAAAAAKBlKddLv7++vNm3aKCIiwrbOarUqIiJCHTt29GBkAAAAAAB4Xokf3p+YmKgjR47YnkdFRSkyMlJVq1ZVvXr1NHr0aA0aNEht27ZV+/btNXXqVCUlJdlm8wcAAAAAoMRLTZWyJ5f/7DOpXDmXVFvik/5t27apW7dutuejR4+WJA0aNEjz58/XgAEDFBMTo1dffVXR0dEKDw/XihUrckzuBwAAAABA8TI5XtRikb7+Omt5/nyXRVDik/6uXbvKKGAyg5EjR2rkyJHFFBEAAAAAAKVDqb6mHwAAAAAA5I2kHwAAAAAAL0XSDwAAAACAlyLpBwAAAADAS5H0AwAAAADgpUr87P0AAAAAAHi9wEApMfHvZRch6QcAAAAAwNNMJikoyOXVkvQDAAAAAFACJSUlKTg4uEh1cE0/AAAAAAAuZTj/lrQ0afBgxd97rwJMJplMpiIn/BI9/QAAAAAAeNwTQ4boo4ULVVFZiXq6i+ol6QcAAAAAwANOnz6tunXrSpICJX3khjZI+gEAAAAAcAeTKccqwzBUq1YtxcTEFEsIXNMPAAAAAICbHT16VCaTST4+PsWW8Ev09AMAAAAA4BZxcXGqEpKzt784kfQDAAAAAOBC50/8oe8WzNai32d7OhSSfgAAAAAAXGHV4nf0588v2Z63rCet2efBgETSDwAAAACAS1yZ8EvSDXUcf2+ypBpXLLsKST8AAAAAACXABTfUyez9AAAAAAB4KZJ+AAAAAAA8zF/StMsPfxfWS9IPAAAAAICH+UoacfnhyuvwSfoBAAAAAPBSJP0AAAAAAHgpkn4AAAAAALwUST8AAAAAAF6KpB8AAAAAgCLKSE/1dAi5cuWkgAAAAAAAlAlWi0UpSZe0eMpDSj+50tPh5ImkHwAAAAAAJ6xb+qH2Lxnh0jpTJIVdsewqJP0AAAAAADhg8+rPFLnwMbfUbUg67oZ6SfoBAAAAAMhD9MmD+n78DZ4Oo9BI+gEAAAAAuIrVYtH+Hav028zexdKen6S3Li+/IinDRfWS9AMAAAAAIMmwWnXpYrQW/euaYm/bT9KLl5fHi6QfAAAAAACXORi5Rmv/193TYbgcST8AAAAAoMyxWixa8+0UnT64XilRSz0djtuQ9AMAAAAAypwZz1STr+WSp8NwOx9PBwAAAAAAQHFZ+eUEzRpmKhMJv0RPPwAAAADAyxlWq2b88zqZk495OpRiR9IPAAAAAPA6VotFn7zRW2kJ0TLF/SGzpwPykEIl/Q0aNJDJZHL6faNGjdJzzz1XmCYBAAAAAHDIpdhofflibUmS85mrZ6RIanrFsqsUKumfP39+oRoLCwsr1PsAAAAAACjIicM7tfzt1p4Oo1AMSfvcUG+hkv4uXbq4Og4AAAAAAArFkpmhT97orYxTEZ4OpcQpVNKfkZGhtWvXqly5cmrSpImqVavm6rgAAAAAACjQ3FdvU8bpXzwdRpH5Sfq/y8sTJGW4qN5CJf333nuvateurW+//VaVK1dWSkqKmjdvrhUrVrgoLAAAAAAACuYNCb+UlfSPv7w8SR5O+k+cOKGlS5dqy5YtioyM1PTp03X8+HEXhQQAAAAAAFyhUEl/uXLlJEn+/v5KT0/XiBEjdPPNN7s0MAAAAAAAUDSFSvqfe+45xcbG6h//+Ieefvpp3XLLLbpw4YKrYwMAAAAAAEXgU5g3Pfzww6patarGjh2rzp0768CBA/r6669dHRsAAAAAACiCQvX0X2nw4MEuCAMAAAAAALia0z39Fy9eVGxsrCQpJiZG3377rfbu3evywAAAAAAAQNE4lfTPmTNHbdq0Udu2bTVjxgz1799fERERevDBBzVnzhx3xQgAAAAAgFdLldTu8iPVhfU6Nbz/gw8+0N69e5WSkqJ69eopKipKNWrU0KVLl9SlSxc9/vjjLgwNAAAAAIC8pSYneDoEl7FK2uaGep1K+s1ms8qXL6/y5curYcOGqlGjhiSpUqVKMplMbggPAAAAAIC/WS0WZWak6dM3esoS/ZunwynxnEr6fX19lZqaqnLlyunXX3+1rU9MTHR5YAAAAAAAXGnv1hX6bWZvT4fhFn6S/nl5+b+SMlxUr1NJ/7333quAgABJWb372ZKTkzV79mwXhQQAAAAAQJY/fv9R508d0IG10+SbctzT4biNn6RJl5c/lOuSfqcm8vvqq69yHcZfs2ZNtWvXzkUhAQAAAAAgJcTFaONHfXVk+YtenfC7k1M9/QAAAAAAuEtyYpw2Lf9IMSf/UOJfx2SJXu/pkEo9p5L+vXv3qmPHjmratKmaNm2qZs2aqWnTpqpTp4674gMAAAAAeLn4i+f1+89zdHT1a/Ix0j0djldxanh/48aNNXv2bPXo0UN//fWXPvzwQ3Xp0kU1atRQp06d3BVjsUhOTlb9+vU1ZswYT4cCAAAAAGVCQlyMvvrgCS0cU0tRq14h4XcDp2/Z17x5czVv3txufWpqqvbv3+/SwIrbW2+9pZtuusnTYQAAAACAVzOsVq1fNlN//Pgf+WbGejocr+dUT//TTz+d6/py5cqpVatWLgnIEw4fPqwDBw6od2/vvPUDAAAAAJQU65fN1P4lI0j4i4lTSf+1116rm266SV26dNHq1aslSWfPntW8efP00EMPuSXAdevWqW/fvqpTp45MJpOWLFmSo8z06dMVFhamcuXKqUOHDtqyZYtTbYwZM0YTJ050UcQAAAAAgKvNefkWzRpm0v4lIzwdSomUKqnr5UeqC+t1anj/s88+qzfeeEPXXnut5s6dqwULFuibb75R3759dffdd7swrL8lJSWpZcuWGjp0qO69994cry9atEijR4/WzJkz1aFDB02dOlU9e/bUwYMHVbNmTUlSeHi4MjMzc7x35cqV2rp1qxo1aqRGjRpp48aNbtkGAAAAACiLUpMTZMnM0OfPV/N0KCWeVdKvbqjXZBiG4WjhVq1aaefOnZIki8WiGjVqaPfu3apbt64bQsvJZDLpu+++U79+/WzrOnTooHbt2mnatGmSJKvVqtDQUD377LN66aWXCqzz5Zdf1ueffy6z2azExERlZGTohRde0Kuvvppr+bS0NKWlpdmex8fHKzQ0VJcuXVLFihWLtoEAAAAA4AW2r12sbZ8N8HQYJcLTc91XtyN5qFPD+2NiYrRo0SLt2LFDKSkpCgsLK7aEPzfp6enavn27evToYVvn4+OjHj16aNOmTQ7VMXHiRJ08eVLHjh3Te++9pyeeeCLPhD+7fKVKlWyP0NDQIm8HAAAAAJQmVotFp47uzrF+zbfv66v/PUHCXwi+koZffjg1JN+Beh32wgsvaOXKlZoyZYr279+vtLQ03XPPPQoPD1d4eLj69+/vwtAKduHCBVksFtWqVctufa1atXTgwAG3tPnyyy9r9OjRtufZPf0AAAAAUFbMfjFcpkt7JEntB3+to5Er9VfkbA9HVbr5S5p+eXm+pJwXqBeOU0n/888/b/c8KipKe/bs0Z49e/TNN98Ue9LvaoMHDy6wTEBAgAICAtwfDAAAAACUIEnxsdqwbIba3zHElvBL0pb593kwKhSkSKMGGjRooAYNGqhv376uiscp1atXl9ls1rlz5+zWnzt3TiEhIR6JCQAAAAC80cKJfWU5v1FRq//t6VDgBKeS/lq1aqlRo0Zq3ry5mjVrZvtZpUoVW5kZM2bomWeecXmgufH391ebNm0UERFhm9zParUqIiJCI0eOLJYYAAAAAMBbzXmls3wDghXWvKcs57nbWWnkVNJ/5swZHTx40Dakf9WqVdq3b59SUlLUtGlTLV++XB999JFLk/7ExEQdOXLE9jwqKkqRkZGqWrWq6tWrp9GjR2vQoEFq27at2rdvr6lTpyopKUlDhgxxWQwAAAAAUNYsfO9hWaLXyyLp4PHlng4HheRU0m82m9WkSRM1adJEDzzwgDZt2qTly5fru+++019//SVJcuIOgA7Ztm2bunXrZnuePYneoEGDNH/+fA0YMEAxMTF69dVXFR0drfDwcK1YsSLH5H4AAAAAAMfF7//C0yHABUyGE1n6hQsX9PPPP2vZsmXauXOn2rRpo169eqlnz56qUaOGpKyJ7lq3bq2mTZuqadOmatasmZo2bao6deq4bSM8KT4+XpUqVXLo/ogAAAAAUBoc3btRq6fc4ukwvMLTcx0rFygp6fJykKRkB97jSB7q9DX9LVu21JgxY/TZZ5/JbDbnKNO4cWPNnj1be/fu1Z49e/Thhx9qz549iouL0w033KD169c70yQAAAAAoJjtiJjn6RDKnDRJd16x7CpOJf2TJk3S3r179d///lfPP/+8QkND1axZM9ujV69eMpvNat68uZo3b2733tTUVO3fv9+FoQMAAAAA3CF21xxPh1DmWCT95IZ6nUr6s6+nzxYVFWXr0f/888/Vq1cvPf3007m+t1y5cmrVqlXhIwUAAAAAuNW2NV9q2w9vyOTpQOAyTiX9V2vQoIGaNm2q1NRU1apVS3369FGrVq20du1ade3a1UUhAgAAAADcKTMjXT998n86u2kyCb+H+Ep6+PLyAkmZLqy3SBISErR//37t3r1bkZGR2rx5syZPnqyOHTvqxx9/VFBQkCviBAAAAAC4ycdPB3g6hDLPX9L8y8tfyXVJv1Oz9+fm/PnzqlmzZo519957r2699Va9/fbbRQqwpGP2fgAAAACl1adv3aOUP3/wdBhezdOz9xc56ffx8VFISIhatmxpe4SHhyspKUl9+vTR+fPni1J9iUfSDwAAAKA0ObJng3ZEzNX1rftoy/z7PB2O1/N00l/k4f2HDx/Wrl27tHv3bu3atUuLFy/WsWPH5O/vr4yMDD3yyCPq0KGDwsPD1alTp6I2BwAAAAAogoj3b5UkbdntYDaKUq3IPf25iY+P15o1a9S/f3899NBDOnjwoPbt26fkZEe+qyhd6OkHAAAAUNIZVqs+fqWTLOc3ejqUMqfU9/RXrVpV4eHhtqH9zZs3V3BwsH766Sddd911WrBggSTJYrEUtSkAAAAAQD7OnTqsvZt/UOe7n5Wvn7+S4mM1f8y1MlsTZTLIycqiIif9c+fO1a5du7Rr1y59//33OnbsmCQpMDBQixcvtpUzm81FbQoAAAAAkI9vXmshszVVcef+VJ2G7bRr0ZCiJ30o1Vw+vD8hIUFnz57VNddcUyZu18fwfgAAAACesPLLCToS8ZZ6jlql65reLEmaNczk4ahwNUeH95sl9b+8/J0kR8ZlOJKH+jjWvOMqVKigRo0aKSgoSHv27HF19QAAAABQplktFqWlJClq1SsyW5O18v2ukqRFU4d4NjAUiUXS15cfrrwQw+UjPRISErRw4ULNmTNHO3bsUGZmpqubAAAAAIAyafuvi7Xt0wF263yMDE0fXke+aWc9FBVKMpf19K9bt06DBg1S7dq19e9//1uhoaFyw40BAAAAAMCrnTq6W9OfrqlZj/tozbfvK/HSX5KkS7HRORL+bCT8pZ9Z0n2XH66cEa9I1/RHR0dr/vz5+vjjj3X27Fndc889evjhh3XHHXfowIEDatmypdfP2s81/QAAAABc5dCuX7Xmg66eDgMuVGpv2de3b19FRESoW7duGj9+vPr162c3cZ/JxAQSAAAAAFCQH+aMkdVqUb8n3yfhh8sVOulftmyZBg4cqFGjRqlt27aujAkAAAAAvIZhtcrk42NblmR7nhAXo7ObJkuSZm2e6pH44N0KfU3/xo0bVb58eXXv3l2NGzfW66+/rqNHj7oyNgAAAAAolQyrVZdio/XdzOc048lA7du2UpI0a0xLffh0FSXExSjmzJ/64oWaHo4U3q5I1/RLUlJSkhYtWqS5c+dq06ZNateunR5++GE1bdpUt99+O9f0AwAAAChzZo9tL+PCVtvzzIA6GvHhac0axmXQZY2nr+kvctJ/pYMHD+rjjz/WZ599pnPnzslkMpH0AwAAACgzjh/arovnT2jzvHvtXzCZZClfX+bkYx6JC57jVUl/NovFoqVLl2ru3Ln64YcfXF19iULSDwAAAMCSmaETR3Zq5aQOng4FJYynk/5CT+SXH7PZrH79+qlfv37uqB4AAAAAPC57Uj5JmvlsPfmmR3swGpR26ZIGX7HsKoVK+hs0aFCoW/KNGjVKzz33XGGaBAAAAIAS41JstBb8K0xmI02Sm3pTUaZkSvrEDfUW6tycP39+oRoLCwsr1PsAAAAAoKSIOrBFKyd1kNnTgQAOKFTS36VLF1fHAQAAAAAlUlJ8rL6YeKc6PfCWzkbt1qFlz3s6JHghs6Sel5d/luSqKfEZhQIAAAAA+fhsdA2ZDKt+nXabp0OBFwuQtOzysqMT+TmCpB8AAABAmTL/tV5KPr9XQ97dq0+fqyRJqtF2hM5GfqH6Nz2lu4ZMVNT+zVr53k2SJOdnMwNKDrfcsq8s4ZZ9AAAAQMmUeOkvHYyMUOtO98nk42NbP2tYVhqfaa4oX0u8p8JDGeGVt+wDAAAAAE/75KWm8k0/p7N/vqS7hkyUJMWc+dP2Ogk/ygKSfgAAAABeyTf9nCTp9G9v68ON/5PZmlTAOwDvQ9IPAAAAoFRb/tk41bmutVrefI8y0lP10yev6NK5I3ZlSPhRVpH0AwAAACjRfl/1icoHVVbSpQtKSrigc39u1XWt7lTHnkP0+6pPdGLt6zqxVmp5s6EfPhqtCztmeDpkQJK0/4ynIyDpBwAAAFACJCfG6eie39SsfR/bpHtpKUn6feVcHfjhuRzldx/+RlVq1tf2b8bYkpols0eR8KNEsToxbX66pBFXLLsKs/cXEbP3AwAAAI7JzEiXr59/ruumD68j37SzqtXhn+r96FsyDKvmvtxW5sRDHooWKLq9p6X//ey++pm9HwAAAECJMOeVTso8t0n9xu1VSGhjSdLWX77QjgUPq1qrp+SbdlaSdG7zfzV/838lSWaPRQt4D5J+AAAAAC6xf/sq/fb1ODVo1U9Hfx6rys0Hy5qZLr9yFWSJ/k0mSd+Pv0EB9XsrMy1BlujfJEl/7Zzl2cCBEsBHUqfLy+slWV1UL0k/AAAAAIclJ8bp/OkjCmvc1rZu//ZVMvmYte7DOyRJR3/eJEmK+2N+rnWkHV/u9jiB0qacpLWXl4MkJbuoXpJ+AAAAAA6b/3yozNZE3fLUT2rWvrd2rPtaWz+539NhAcgDST8AAABQxh3Y+Yu2Lv+f+j41XZWr1dGRPRu08Yf31GfIFFWv3UBrvp2qQ8uel/T3dfYbZvXRBkblAyWej6cDAAAAAOBZv067TclHl+jr97J67FdP7aKUo0v09aS+kmRL+AGUPvT0AwAAAF7q2xkjdSFqs24fOl0rZg1Sy55j1PGOIZr7ajeZ/QKVeumkGt86zFbecn6jZg0zyXT5uenSXs0aZsq9cgClAkk/AAAA4KVitk2XJK2c1EE+kv746nEFV6qpzLPrlCnJJOnQstGeDBEo86655hqdPn3abfUzvB8AAAAoZawWi+aO66Glc8cqLSVJc/6vk1Z+OcGuTEJcTK7v3TTn7uIIEYADPvnkE0VFRenixYu67x//yLNcQEBAodugpx8AAAAowX769FX5mM3q9fA427rffpqljFMROnMqQj+lp8hy7jdFrfpNP6WnKur3OWrZZ5z2fve0B6MG4IjHHntMklS5cmWFhIbqxcvrM64qN3z4cL3//vuFaoOkHwAAACiB9u9YrS0/vq/U4z9JkpYbhno/Ml6SlBz/dy/++a3/sy2f/PUN+Uok/EApZDGbNdkN9TK8HwAAAHAzw2q1/bRaLHbrrixz5bp102+3JfySdGLNa4r87TtZLRYZhlEMUQMoTu76vaanHwAAAHCjI3s2aOV/e6hGi0cUGzlHkuQfeodST/2qG/q8rW73jtKcVzrJEv2bDJlVq/0Idej1VK51bZ53rzbPK87oARQXk9WqtpeXd0i68mtBk6nwd9Eg6QcAAADcaNWcx2W2ptoSfklKP7lSPpIOLXtegRWryRL9myTJJIvOb/lAS/YsldlD8QLwDD+LRVsvLwdJSnZRvQzvBwAAANwq/yG7kQsfy7HOnBzlrmAAlDH09AMAAABXsFosmvVCM/lXCNGwN9bkeH3d0g+1f8mIrCeVW6jf819rybhGOcp1H7VOv0ztTC8bAIcUZQh/fkj6AQAA4PWSE+OUmpygKtWvkcnHRxnpqZKkzIx0lQ+qaFf2j80/yifhgDITDuivcydkMplULrCCAoMrS9LfCb8kxe3WN2/3yHUo/i9TO7tpawDAcST9AAAA8Gpnju/T0tebSpIsQQ311JR9+mh4VZmNFElSr7HbVL9RG1t5S+bfd8j++v/q25YrNX1UF09sydFzb0454b7gAZRqJeFGG4w2AgAAgNe4MmHPtv7bd23L5qQjOnNsry3hl6S1i8YpLSVJKUnxSktJktWSmWvdl/Z+Jp+Eg64PGgDciJ5+AAAAeIVDu35VxAc9VLnJQ3rohU9t6zPT7efA/mliK7vnqceWaf7I4GKJEQCKG0k/AAAAvELE/Gfko0zF7/tM0t9Jf9zx3/inF0CJZ/Hx0fjLyznHLBUen38AAAAotf46d0JLZz4p34AK8onfb1s/a9jfs2DzDy+A0sBiNus1N9TLNf2SoqKi1K1bNzVp0kTNmzdXUlKSp0MCAAAos6JPHtT+7askSRnpqdq25kulp2YN0f/j9x/117kTOrTrV5368w99PflepZ34WUmHv/ZkyABQZIabZv3ji09JgwcP1ptvvqlOnTopNjZWAQEBng4JAACgzPp+/A2SpHJjftevi8cr7cQK7Vx5k1rd8U9t//whu7KZftX4hxaAVzAZhppcXt4v6cqvAEwmUy7vcEyZ/4zcu3ev/Pz81KlTJ0lS1apVPRwRAACA9zCsVn38ny7y9Q/W4HHL8y174WyUFk+83XbP+5/+11++aWclSdbzv2v757/neI9vxl+uDhkAPMIvM1N7Ly8HSUrOr7ATSvzw/nXr1qlv376qU6eOTCaTlixZkqPM9OnTFRYWpnLlyqlDhw7asmWLw/UfPnxYwcHB6tu3r1q3bq0JEya4MHoAAICyKz01WVt/+UKW6N+UdmKF0lOTdXj3Op06uluJl/7SH7//qNTkBEnS8UPb9c2/r5U56ajt/dkJPwCg8Ep8T39SUpJatmypoUOH6t57783x+qJFizR69GjNnDlTHTp00NSpU9WzZ08dPHhQNWvWlCSFh4crMzPn/VZXrlypzMxMrV+/XpGRkapZs6Z69eqldu3a6fbbb3f7tgEAAHizj0Y3tEvc5/5fB5ku7bErs0Fm3fnyNq14p21xhwcAZUKJT/p79+6t3r175/n6lClT9MQTT2jIkCGSpJkzZ2rZsmWaO3euXnrpJUlSZGRknu+/5ppr1LZtW4WGhkqS+vTpo8jIyDyT/rS0NKWlpdmex8fHO7tJAAAAHnN49zr9uWedyleoqk53Pi2TT/4DP88c36fDkRHq3HeEreyl2Gj9vmKOzv25RY06/ENpyfHyCwhU3PljqlwzTFVq1NPaz57N0VN/dcIvSSZZ9NPEVq7bQACAnRKf9OcnPT1d27dv18svv2xb5+Pjox49emjTpk0O1dGuXTudP39eFy9eVKVKlbRu3To99dRTeZafOHGiXnvNHTdSAAAAcL9f/tvFtpyRlqzb7huTb/mlrzeVJKUmXVLPgf+WJC149SaZU45LknZFLbUrf/LyzxJ/DSkAlBGlOum/cOGCLBaLatWqZbe+Vq1aOnDggEN1+Pr6asKECercubMMw9Add9yhu+66K8/yL7/8skaPHm17Hh8fbxslAAAAUNIc/mO9TCYfxcWcVMyp/XavHVn+ooIr11KlatcoIz1Ff+7+Rae3fKiAWh1kGFYFVr7GVvZYxH80ff101W090JbwAwBKvlKd9LtKQZcQXCkgIIBb+gEAgFIhJSlev0ztnG+ZyIWP2T03S8o8+6skKSHavqxverSif5/iyhABAG5WqpP+6tWry2w269y5c3brz507p5CQEA9FBQAAUDJcvHDK0yEAABxk8fHRpMvLGS6st1Qn/f7+/mrTpo0iIiLUr18/SZLValVERIRGjhzp2eAAAACclJ6arI9GN5Jv2mnbukxzRbX+x1SFNmyjH97tIpMlST6GK/8dBACUBBazWf9yQ70lPulPTEzUkSNHbM+joqIUGRmpqlWrql69eho9erQGDRqktm3bqn379po6daqSkpJss/kDAACUdImX/tJ3Hz4lX/9ydgm/JPla4rV78VD9Ub29zJlxngkQAFBqlfikf9u2berWrZvtefYkeoMGDdL8+fM1YMAAxcTE6NVXX1V0dLTCw8O1YsWKHJP7AQAAFJfY8yeVeOmCKlWrrcyMdElStVr1dPHCaVWpfo0uXjitipVr6nTUHlWoUlNfT75f1vP533nIuLClOEIHAHiK1ar6lxdPSDKueMlkMhW62hKf9Hft2lWGYeRbZuTIkQznBwAAJcKCdx9U4sFFOdZbgq6TOemojCrhMl2MLP7AAAAlml9mpo5dXg6SlOyiekt80g8AAOBOZ47v06Yfp6nHQ6/q7PF92rNhsUwmH/kFBMrsF6C0pDi17/mUfP0DtOGH/+q2B/+tAztWKfKnd2VJjpaPNVltHvxY7W8bqCWzn8814Zckc9JRSSLhBwAUK5J+AABQpi2ZcIvMmXFadHyrjL+25Vrmhx3zZPgGyTfjgr78c6NMcbtk0t//SO384mG1v22gzm2eWlxhAwDgEB9PBwAAAOCsAzt/0ZxXOunE4Z36feV8ffyfboq/eD7f9yx872F9N+ufkqSM9FTNHXeH1nw71TY5Xl4JvySZjRT5ZlyQJJniduVaZtawwl9vCQCAu9DTDwAASp210+6QSRYtndJHvunRkqTvpg3VoP/8mGv5qP2bFb//C8VL0lP/1Y/zxirj1CodOrWq+IIGAMADSPoBAECJFHVgi9Z99bpuf+xd1anfRBfORmnZnOfU4a7nZZJFkmwJvyQlH1+lGaNulMnHLNOlvZIkq085+VhT7eqlRx4AUJYwvB8AAJRIyyd3U+qxZfrunTskSd+810+px37Ur9Nuy7W8j5Eun4QDtoRfUo6EHwCAsoakHwAAuFXU/s1KiIuxWxdz5k+t+2G6JOnUn3/o3KnDWjj5Ef2+6hMZVqusFovM1qybFfmmndaiqUOkuN3FHjsAAMXFYjJpuqTpkjJdWC/D+wEAgNvs37Fa66bfLqvJX8/MSbOt//Y/10mSTh5Yp8SDi23rd+1boJhT+3VqwxS7nom4P+YXU8QAAHiG1c9PI91QLz39AADATtSBLZr+TC0t/2xcod6/ZPYoTX8mROdOHdYf6xdKyhp6n5srE/5sJ7bMl4+RUai2AQCAPXr6AQCAnRUzH5Vv+nmdWPu69OhrOrJng44f2KTb7hsjSVq75APVqtdEN7buocgNS3Rgy3e6eHKHgms0VkZyrDLOrJGvpCXjGtnVO2uYSUalZjJd2pNv+77p59y1aQAAlFyGoeqXFy9c9ZLJVPhJaEn6AQCAHSPTfvK7iPdvlSSVD6qiClVq6eDSf+qgpBs/NrR5bn9JWUMHkwtI5iUVmPADAFBW+WVkKHsGnCBJyS6ql6QfAAAvdOrobv3wwf1q1uOf6tx3uN1rm36epx3fjZVvRowyA2pr0MQ/tGB0Vt/CwMnnZU45YSt75e3t/vj6cbt6uPUdAAAlH9f0AwBQShlWa57rlv5vgMyJh7R/yYgcr+1ePFS+GVl9Cb5pZ/XDR8/aynz2Smt3hgwAAIoZPf0AAJRCqckJ+nh0AwVUvVGPT1gvSfpi0kNKOPClMgOukaxptm/25756mx75v6X66PkG8k0/n6OuhP0Lbcvm1FPFET4AACgmJP0AAJRC65dOk2/GX7Kc+822LuHAl5Ky7mt/pYzTv2jL6s9yTfgBAIB3I+kHAMADPpvQX/GnNmvIu/sUUC5Is0bfKP8KtfX4W+ttZVKTE/TxmBvkm3Ym37ocubZ+73dPFzlmAABQ+pD0AwDgAclHl8hXUsSiCarbqIPMSUdlSTpqV+aXrycVmPADAADvZxhGod9L0g8AQAEORq7Rrl8/V+d7/6W1X0/QLX2fV2jDcLsy+7ev0h+/fal7nvxAf+7bqHUf3qHAhv316Mvf6rcfZyku5pjuGjJRxw9t16ZlH9jed2bjJJ3Z+Hc9H46sb5s9PzOgDn+oAQAoIywmk+ZfXs50Yb38LwEAwBUunI2Sf7kgVaxS07Zu7f+6S5K+3z1XkrR039ca/lGS3fvWfXiHJGnJTIvi9nwiSUo+8p3i/jpjG1p/qHUvrZ7WS2Zrap7tX3m7PHr5AQAo3ZzpoLf4+mqIG2Ig6QcA4LKEuBh98+9rJUlPfZz3X2mzNTnP1+LO7LZ7npwYZ1v+K/rPfBN+AAAAVyPpBwCUKeuWfqgDv32iAWO/1xdvdJdP/H4FNx6glEtn1Lb387Zys4aZZA65VRnJf9lufXelPCfPi91p93Tp601ty7sXD3XFJgAAAG9kGAq8vHh194LJVPCkvXkh6QcAlErnTx9R9ZAG8jGblZmRroz0VJUPqihJSrz0lyQpuFI1WTIzdDHmlKrXbiBJ2r9khCRp4Vu95BO/P6v8wUWSpM0/2P+JtUT/lmvCDwAA4Gr+mZnKvngwSDkT/8Ii6QcAlDq//TjLdp38Ux8bmvVsHflm/KWHp1zQ6ajdtmvwr+35tv78+SVJkrnmzXp84gZbHaa4XTkr/mu7+4MHAADIRVFm6M8PST8AwC3SUpK0f/tKteh4t3zM5nzLHt69TpWq1VHNaxrq6N6NCgyuotr1b7Qrs2vj9zq+b70y01MUs/1Du9d8M7J69j9/rasMS5qtdz474Zcky/mNmvWvtkXeLgAAgNKEpB8A4Bbz/n2LTHG7tH/zw3rohc/zLHfq6G798t8ukqR73ziq1VNukWQ/kd6F6GP6/eN+ub4//uJ527Lp0h7le8UbPfkAAKCMIekHABSKYbXq6L5NqhYSpuSEi7qmQTO717OHz8fvWyApK+m3ZGZo8+pP1fymu1Whcg2dO3VYaxaNs71n1Rf/ti0f/mO9/PzL6/ypQ9q3Ie8vDRaOqeXCrQIAAPAuJP0AgEL54eMXFf37FNvze8YfUEho43zfM2v0jTInHdUfX0kDJ5/XknGN7F5P2L/QtvzL1M6uDRgAAKAMYlJiAEChnNo6x+75/q3LC3yPOemobfnEYYbaAwAAuBs9/QBQxmxc8bF2Ln1Ntz0+X4e2/6RTu77TwPHrVblaHbtyhtWq2U9kTcBXu+MYnd30niQp06+6fDMu5PgDcmjZ8zq07HlZTX7yMTLsXps9toOMC1vs1v02s7drNwwAAKAUs5pM+uryssWF9dLTDwBe5uzx/fr2wxG6FBud6+t/fPW4fFNPKmLmP3R202SZk//U0tnP5Sh3ZM9vf9d5OeGXJN+MC/m2f3XCLylHwg8AAAB7mb6+ekDSA5LSXFgvPf0AUApZMjNk9vXL9bVvJ9wq38xYfRn1u56atD3PsmZLgm05I+WiMtJTZcnMkI+PWRnpqUq8FOO2+AEAAMoCo+AibkfSDwClzPzXeivlZIT6/t921b22eY7XfTNjsxZid+jbGc/q/PYP1e6RhWrT9QG7cibj74FjGad/0dxnyrs1bgAAABSOyZTvTYnzxfB+AHARw2rVmm/f16mju/Msc3TvRq3/cYbdul0bv9f2Xxfbnl+KjdaqRROVnBgnSYo586dWL35XMWf+1IfPXa+0EyvkY2Ro2VsttGLBa8pIT5UkbVn9ufZuXWFXd8y2aTIZVm37bIC2RHyh6cPtr9sHAABAyeCfkSFDWaMDAl1YLz39AOAiK7+coGMR/9GhZdJTH+c+mGv1lFskSYEVqqlNlweUkZ6q3z/uJ0lq2LyzKlUN0YLxt8qcdFTHdy/X42+t0+Jx4fK1Jujoz2Nlvqq+47+M1zfnjqrzP17SzoWP5hvfzi8e5kMfAACgjOH/PwAopA0/faTIH8epclhnGdZMXfpzte1D1ZKZoVnPVJZkUmjHEeo79B199HJH23u3fPcfRe2OUK/HJtjWfflibUmyJfaW6PWaNcxU4Af1pb2faenez1y1WQAAAPAiJP0AUEh7vnlSvpISDy6SZP+B+vnEe2W2JkuSzmx4V8dvfUDW87/bXvdJOKS/Ig9pR/0WxRgxAAAAyhqSfgBeIfb8SUV8+bpuvWe0ate/Md+yVotFS2aPUqUa9RV79qB6PTpBuzcuUcLFM+r18DhbuaT4WP30yVhVrd1YR5a/qPLX3q27n56hb6c+oowza/JtI/XYj3bPd2/4Ktdy+78f6eAWAgAAAM4j6QdQ4u1Y97XKB1fWja175Flm8cReMsXv07f7vtOImfnfR375568qZts0Zd+QbvG5w8o8+6skKap1LzW4sUPW+ikPKf3kSsVGZpVL+fMHLZpwTIrLe6K+vJz57R2n3wMAAAAUFUk/gBIt5syf2vrJ/ZKkG6+YHC8pPlYmHx8lJ8apWs16MsXvkyT5ZvwlSboQfSxrvY+PMtJTlZQQq/jYczIMq07uXqYrb3qSFrPLdh394cjVSk68qMyMNKWc2ZRj4rzCJPwAAACAp5D0AyjRzp06mGNdRnqqPn++Wp7v+ep/Tyg2co5M1drqyXe3avZzdW1fBkjS1Xc5NWfG2ZajVv9bUasvry9K4AAAAIATrCaTll1etriwXh8X1gUALufj83fq/eFz1+vw7nX69I3e+b4nNnKOJMn4a5sk2SX8AAAAQEmU6euruyTdJSnNhfWS9APwGMNq1dZfvtCF6GM5XrsUG63Nq+1vQ2dOOqJf/ttFmWfWOtzG4v8OK2KUAAAAQOnF8H4AHvPr9//TwR9HaZvJX8/Msf8+8/P/aybfjL8UUK9nkdq4uHtukd4PAAAAlGYk/QBczrBa9dmEfqoc0kh3P/5ermV+X/WJDv44SpLkY6TLkpmhOU/5217P/nBKO/Gzm6MFAAAAPM8/I0OJl5drSkp2Ub0M7wfgcrs2/aCUqKU6u2ly3mW+HGz3/Mspj7o5KgAAAKBkC7r8cCV6+gHkKSUpXvPGtlDlsJs1cMwXkqTZ/2pnmyDPP/R2pZ9cJYtPoCSrDJO/fC3xdnXMGmY/V36rgQsUueqDHG0lHlzkno0AAAAAyjCSfgB5WrXwDZlTjith/3FJWUl/dsIvSeknV0mSzNbswUepBda584uHXR0mAAAAUOoZhuGWekn6gTLs87fvU/LFk3rwpaVaMP5W+SQeliSVb3C3UqJ+sCu7/dfF2vz1S9y7HgAAAChFuKYfcLO/zp3Qx//uqh3rvvZ0KDkkHf5GxoUt+uKN7raEX1KOhF+Stn06QObkqOIMDwAAAEAR0dMPuNk37w+QEfO7tn7yq1p3znvIzpE9G3R09xrVa9xBZ6J26bb7xtheO3N8n7b/8qlkGArv/JASL8VozacjFVT9eg14YaG2RHymilXr6MbWtyviq7fVqstAHdj+s2rUbaxm7XtLkk4d3a0f/nuvzElHVS7sTqUcW6Hsq+1Nl/a6cxcAAAAA8BCSfsDNMuJPOvSLFvH+rZKkYxFZzyvXqKc2XR6QJC15s43M1qzr5U9veE8mWWWWlJp4SJ+O7267zn7P+vuUdPhrnfz1TUnSIUnN2md90fDjhNYyyyJJSj22TPbT6wEAAADwJMNk0trLy1YX1svwfsDdCpldnz68xbacnfBnVWf/EXDlxHqXjv+WTxiWwgUCAAAAwO0yfH3VTVI3OTI9tuPo6QfczDf1dI51X/3vCQVXuUa9HxmviK/f05HlL+Yoc3bTZM3K5z73ubaVHp1j3dW3zAMAAABQuphMhf+fnqQfZYZhtcrkU/jBLVe/37BaZRhGvr+AyYlxOdYd3r1OsZFzFCvJ8uAruSb8AAAAAOAKJP0oE+a80llpMbs1aNJRBVeq5vT7Tx6J1NK3b1blxvdo4IsL9f1HLyj69yl2ZTL9qsk3468C64qP/bs3fs5T/k7HAgAAAMD7+Gdk6Pzl5TBJyS6ql2v64XXOHt+vYwe3KfrkQaWnZv2qWKLXy9dySWu+nWQrd/70EaUmJygtJUnnTh3WmeP7dOb4Pu1Y97WSE+NkWK3avnaxrBaLVnw8XGYjRQkHvtTJI5E5En5JDiX8l2KjdfLQJtdtLAAAAIASy8j75l25qnH5cTWG9wOX7Vz/jbbMv8/2PNOvukbMjPm7gDVrErxTf/6hZW+1kMUnUIZPgHwzL9rVs/UTKdM/RL7p0dr2mWSq0cH22k8TWxU6vi9frF3o9wIAAACAs+jpl/T++++radOmatKkiZ577jkZzn4dA7e5eOG0LsXaT04Xf/G8Ll44LcNqVfzF87Jasmalj/vrjHb8/IFdWd+MCzKsf892n56aqOiTB7X687GSJLM1OUfCb3vvFZPiZSbmnCAPAAAAAFzFXXlome/pj4mJ0bRp07R37175+fmpc+fO+v3339WxY0dPh1bmLXj3QSUeXCRJ6vrsL2oc3k1RB7Zo5aSsXndrhcbySTgoS3Aj3djlGR1a9nyu9cz4Z0OZLy9f2DFD3++Y4XQs5pTjhdoGAM5pfNdUHfxxVIHlLOYKMlsS3B8QAABAKUdPv6TMzEylpqYqIyNDGRkZqlmzpqdDgmRL+CVp0/fvSJI2/vD3Lex8Eg5KksyJh7Rvxfg86zEnR7knQAAAAAAo4Up80r9u3Tr17dtXderUkclk0pIlS3KUmT59usLCwlSuXDl16NBBW7Zscbj+GjVqaMyYMapXr57q1KmjHj166LrrrnPhFniP5Z+N00cvd7RNjnelL6c8pk/fuiff9xtWq+a+epu+nTFSCyc/ok8n9JeUNbndrH+11fLPx2vGqMaaNcykD5+91u69aSd+1qxhJl06vDTXun0tlwq5VQAAAADgvUr88P6kpCS1bNlSQ4cO1b333pvj9UWLFmn06NGaOXOmOnTooKlTp6pnz546ePCgrcc+PDxcmZmZOd67cuVKlS9fXj/++KOOHTum8uXLq3fv3lq3bp06d+7s9m0rbU6sfV2StOLzV3X34+/Z1htWqy7t/UySdPzQdtVv1CbX92/95QtlnP5FMad/sa07eSRSvyz8j/TXdp1Ys932LVRevfNma4oLtgQAAAAAShbDZNLWy8vWfEs6p8Qn/b1791bv3r3zfH3KlCl64oknNGTIEEnSzJkztWzZMs2dO1cvvfSSJCkyMjLP93/11Vdq2LChqlatKkm688479fvvv+eZ9KelpSktLc32PD4+3tlNKvWSE2LsnlutFttyWkpi3u9LjM1l3UWlJTBJHgDnMN8qAADwNpl+fmrvhnpLfNKfn/T0dG3fvl0vv/yybZ2Pj4969OihTZscuxd6aGioNm7cqNTUVPn5+Wnt2rV68skn8yw/ceJEvfbaa0WOvTS7tOdTzXh+i56evFdb13ypnV88bHttzQddtcaJutb+r7vrAwQAAAAAL2IymQr93hJ/TX9+Lly4IIvFolq1atmtr1WrlqKjHes9vummm9SnTx+1atVKLVq00HXXXae77747z/Ivv/yyLl26ZHucPHmySNtQGlyIPqYf5rxot84n/oBWfvmWXcIPAAAAAChZSnVPv6u89dZbeuuttxwqGxAQoICAADdHVLJ880qDXNcfi3i1mCMBAAAAAO/kl5Gh7JnNmkhy1WxmpTrpr169usxms86dO2e3/ty5cwoJCfFQVN5l18bvPR0CAAAAAHg9k6SwK5avZBRhQqNSPbzf399fbdq0UUREhG2d1WpVRESEOnbs6MHIvMfvH/fzdAgAAAAAgEIq8T39iYmJOnLkiO15VFSUIiMjVbVqVdWrV0+jR4/WoEGD1LZtW7Vv315Tp05VUlKSbTZ/AKVHpl8N+WbEFFywGFhN/vIx0j0dBgAAAFAkJb6nf9u2bWrVqpVatWolSRo9erRatWqlV1/Nup58wIABeu+99/Tqq68qPDxckZGRWrFiRY7J/eC8/dtXeToEeJDFXEGPvP+X2+oPbjwgx7oRM8/bPQ/t8p8itWEpXz/P15r9Y3a+731mTpqe+th+GFVmudAixSNJmQG15XeNc3etyAyoU+R2AQAAULIVZQh/fkp8T3/Xrl0L3PiRI0dq5MiRxRRR2XHi4GZPhwAPMmSSycez3wt6un0AAACgtOM/auTp5N6fPR0CvJgj9xotyv1I4d1Mcs834QAAAN6mxPf0w3Ms0b95OgR4lEkmk4d7+ovaPl8aAAAAoJQwJO29YtlVSPoBAAAAAPCwDD8/NXNDvQzvB5Ark8nk3uH1Dg3vL1kfUS4bUu6mSVoAAADgnYryf3nJ+o8aQIlhyOS2GUQlyaTiGHrP8H4AAACUbST9APJgktWS6ekgAAAAgDLBLyNDeyTtkVTehfVyTT+APLmzp9+hSfaYiO8y9gMAAIC3M0lqesWyq9DTjzxZfII8HQI8qGJYN5UPqui2+pvePMDuuSFzjjLNOvZzW/sAAABAWUBPP/JkrnidFLfb02F4XIsH5mrP2o9kPb/Jts7iE6ybB3+muPPHdfDHUU7Vl2muKFNANZmTo/It1+25tfrlg+4yyZrjtdodX9D1rXtp3fTbL9dZSb6WS5Iko1JTteozVmnJ8crMSNWBFeNktiZJkkK7/FuJF8+oVbdBSk2O18aP+ubZfp2GHWT29dM/3vxTf2xaooYtuunEoa1q0r6P4mOjJUkXzh7VpQsn1abbwzp/6pAqVKklX19/yWTS9l8+17GI/0iSuo9ap9hzxxRYoarMZj/5BZTXNQ2aK3uPhtw0Wt3vf0mS9I83/9T2NZ+rTfdHVT0kTHe+slvL3mrh+A4u8ei1BwAAQPEh6UeeDMNaqtKTTP9a8k0/5/J6O/YcorN/blfMFUm/uXJjhd/ST5kZ6U4n/TKZFVApVJkFJP2NWnbRL5VulC7tzfHaje3v1vUtOmvd5ee+la6VYndKkvyDaqpDj0dtZc8d26n4fQskSX0ee8Ouno0fFRxu9doN1O3e5yVJoQ3DJUlVql8jSarfqI2tXKWqIXbva9XlQVvSX6lqbV3fvJPd68mJcbblBs26qkLlGrb2eg78j+21utc2zxFTpl81+Wb8VXDwLj6DDZfeMRVFwZEAAAClQUm4aRPD+5GPEnCGOqX0fEXh1mvlXYXr6QEAAIBi464cgaQfeSsNiWmxyT0Bdut97PPjZQm5x/YjAAAA4OVI+pGn8lWv9XQIJVdJzlHLRALt4DaWiX0BAAAAb2BIOnb54cruV5J+5OmmvqNty73GbvNgJN6IURQAAAAA/pbh56cGkhpISnFhvST9QBGYTG7+FeISC6/EhIAAAABwRlEuhyXph0Pcnty6ghuHcpfqa8754sB12JVuVyomuQQAAChFSkEmB5Q8psvXlBf6y4Ai5jWl4ksYuE4p/s4JAAAAjvHLzNQWSVsklXNhvb4urAvwKJPJ7La6zb4BrqvMx08+vv6yuK5G5IlsGQAAAKWDyTDU7vKyK7v46C5EoZiqtyu40FWqtXrKoXL+oXfk+VqmTwW75+0Hf21bbnDT0L/LmSuqYpNH7cpe3+e9AtvOLBf697JvZVVt+bgkqceDryjTPyRHeZOP/a9QZrlrFFCvl1o+ON9uvXHFFxLdH/9cdz45U5l+1WQ1+RUYk9NK46UIpTFmeJSJay0AAAAcQk8/CuXJd7ZIkqIObNHKSR0kSY/+96ICgysX8M6ZtqVZw7ISPWvFG/TM+/vtS41uJtOlvXbrnvo493/yW3XKWh/x1STbumdmxMrHbNbG5V30x9dZifstfZ5W93+8YGvXqNxSprhddvV/8kZfpR47KUkaMeui7bXA4MoaMeOs7b1Xspr85GNkZL1n+inb+ptuH5RrvNlGzLygL94bqIT9C/MsY8goRF813+UBAAAApY275hEjO4BDHDkBC3+duWtObtdMAEbvIQAAAICSpSi5Dkk/vFL2lxSGYXVXA38vF+XLhkK+15lvAUvC7eE8NfGg678t9fy+BAAAAJxB0o+yi+vI81Wqb1N4WWEujiiR+K4BAACgVHLm3zh33bqYa/oBL+MFuToAAABQJsW4oU6SfriM24bSl2l08XolN32LCwAAgNIr3c9PNd1QL8P74RBPXZPtSq4drl6Su9NLcmy584bzCwAAAHCXouQy/KcNl3Fp4laIntCSMGGdswp73Q5JsmO85lIHb9kOAAAAFDsyB3icyQ0ZjcmHU9uGoeQu5Ip9SQbvGpzXAADAu/hlZmqNpDWSyrmwXq7ph8MyfavIN/OiMv2q29YFVahqW/bxMXsiLNciQQYAAADgASbDUNfLy67swqQ7FA6764VVCqjfW3ePWWVbV/OahqrZ7lnVuXWsAsoHeTC63LltcsErx42XsI5bb7jVnuuwLwAAAFC20dMPh9Vv1EaDX/0px/r+T3/ggWiKrlSkg4UaeFAqtqyYsC8AAABQttHTD8fQe+wmhZ3Ij+MBAAAAoGAk/SiR3DETP5P7lVx8iQEAAAC4B1kQPM9VCZ+7rt8v5Qp7W0AAAAAApR/X9AOF4v090yaT674T9FhPvrccJr63AQAA8HqGYSjJDfWS9AMlWiGyPYbKlwJk8QAAALCX7uenYDfUy/B+OIRrrt2ksEPv8zkeJq/p3oYNhxQAAKBMK0o+RtIP7+ZkTl2YCQRN9NqWYGTLAAAAKNtI+lF2FeHbMgY+lA6MegAAAEBp4ZuZqR8l/SgpwJX1urAuwIWc7z0vaJZ6V05MV7qU/JEIpebYcCcEAAAAuImPYejOy8tmV9brwrqAQjHojc0HE/kBAAAAKDySfjjE5MOp4g4FjU7IS/494yT9AAAAALKQyQGFQmJdKjDqAQAAAGUcST8AAAAAAF6KpB8AAAAAAC/F7P1FlH1Ndnx8vIcjcb3ExCSlpGctJyQkunwbs+u2plly1J2SZpFPun35gtpPSk611ZldNik5xW6dr5+/7bnSLNIVbcTHxyslNUNpV9WRW8w+aZm211PSDZkMx2LMUV9qxt/xXCU+Pj7X/SBJCYlZxyO3bTFSM+ziSL6ijRz7OY+2paz9WZRjnpCQcMX5kyD/IPu6khP/jj8xMSnftq6OM9Owyjej4BiMNItMeWzjledGbv4+vle0K6t883mPIzJNVhlpmcp0op5Mn6K3W1pc+XucH4uPIbPV/fEAAAAURXpm/q9f+T9wWnq6sp9dPfNXWlparu93ZI4wk1HYmcQgSTp16pRCQ0M9HQYAAAAAoIw5efKk6tatm28Zkv4islqtOnPmjCpUqCCTF04a1q5dO23dutXTYZQ63rzfStO2laRYPRFLcbXprnZcWW98fLxCQ0N18uRJVaxY0SV1ovQrSZ8RpYk377fSsm0lKU5PxVIc7bqzDf7GoagMw1BCQoLq1KkjnwLutMbw/iLy8fEp8JuV0sxsNvPhUQjevN9K07aVpFg9EUtxtemudtxRb8WKFUvMOQHPK0mfEaWJN++30rJtJSlOT8VSHO26sw3+xsEVKlWq5FA5JvJDvkaMGOHpEEolb95vpWnbSlKsnoiluNp0Vzsl6fjBO3GOFY4377fSsm0lKU5PxVIc7bqzjZJ0DOH9GN4PAPB68fHxqlSpki5dukQvCADAq/A3DgWhpx8A4PUCAgI0btw4BQQEeDoUAABcir9xKAg9/QAAAAAAeCl6+gEAAAAA8FIk/QAAAAAAeCmSfgAAAAAAvBRJPwAAAAAAXoqkHwAAAAAAL0XSDwAo006ePKmuXbuqSZMmatGihb766itPhwQAQJHFxcWpbdu2Cg8PV7NmzfTRRx95OiR4CLfsAwCUaWfPntW5c+cUHh6u6OhotWnTRocOHVJQUJCnQwMAoNAsFovS0tIUGBiopKQkNWvWTNu2bVO1atU8HRqKma+nAwAAwJNq166t2rVrS5JCQkJUvXp1xcbGkvQDAEo1s9mswMBASVJaWpoMwxD9vWUTw/sBAKXaunXr1LdvX9WpU0cmk0lLlizJUWb69OkKCwtTuXLl1KFDB23ZsiXXurZv3y6LxaLQ0FA3Rw0AQP5c8fctLi5OLVu2VN26dfXiiy+qevXqxRQ9ShKSfgBAqZaUlKSWLVtq+vTpub6+aNEijR49WuPGjdOOHTvUsmVL9ezZU+fPn7crFxsbq8cee0yzZ88ujrABAMiXK/6+Va5cWbt27VJUVJS++OILnTt3rrjCRwnCNf0AAK9hMpn03XffqV+/frZ1HTp0ULt27TRt2jRJktVqVWhoqJ599lm99NJLkrKGPd5+++164okn9Oijj3oidAAA8lTYv29XGj58uLp376777ruvuMJGCUFPPwDAa6Wnp2v79u3q0aOHbZ2Pj4969OihTZs2SZIMw9DgwYPVvXt3En4AQKngyN+3c+fOKSEhQZJ06dIlrVu3To0bN/ZIvPAskn4AgNe6cOGCLBaLatWqZbe+Vq1aio6OliRt2LBBixYt0pIlSxQeHq7w8HD98ccfnggXAACHOPL37fjx4+rUqZNatmypTp066dlnn1Xz5s09ES48jNn7AQBl2q233iqr1erpMAAAcKn27dsrMjLS02GgBKCnHwDgtapXry6z2Zxj4qJz584pJCTEQ1EBAFA0/H2DM0j6AQBey9/fX23atFFERIRtndVqVUREhDp27OjByAAAKDz+vsEZDO8HAJRqiYmJOnLkiO15VFSUIiMjVbVqVdWrV0+jR4/WoEGD1LZtW7Vv315Tp05VUlKShgwZ4sGoAQDIH3/f4Crcsg8AUKqtXbtW3bp1y7F+0KBBmj9/viRp2rRpmjRpkqKjoxUeHq4PPvhAHTp0KOZIAQBwHH/f4Cok/QAAAAAAeCmu6QcAAAAAwEuR9AMAAAAA4KVI+gEAAAAA8FIk/QAAAAAAeCmSfgAAAAAAvBRJPwAAAAAAXoqkHwAAAAAAL0XSDwAAAACAlyLpBwAAXi89PV0NGzbUxo0bXVrvihUrFB4eLqvV6tJ6AQBwFZJ+AABKmcGDB8tkMuV4HDlyxNOhlVgzZ85UgwYNdPPNN9vWmUwmLVmyJEfZwYMHq1+/fg7V26tXL/n5+WnBggUuihQAANci6QcAoBTq1auXzp49a/do0KBBjnLp6ekeiK5kMQxD06ZN07Bhw9xS/+DBg/XBBx+4pW4AAIqKpB8AgFIoICBAISEhdg+z2ayuXbtq5MiRGjVqlKpXr66ePXtKkvbs2aPevXsrODhYtWrV0qOPPqoLFy7Y6ktKStJjjz2m4OBg1a5dW5MnT1bXrl01atQoW5ncesYrV66s+fPn256fPHlSDzzwgCpXrqyqVavqnnvu0bFjx2yvZ/eiv/fee6pdu7aqVaumESNGKCMjw1YmLS1NY8eOVWhoqAICAtSwYUN9/PHHMgxDDRs21HvvvWcXQ2RkZL4jHbZv366jR4/qzjvvdHIvS8eOHct1VEXXrl1tZfr27att27bp6NGjTtcPAIC7kfQDAOBlPvnkE/n7+2vDhg2aOXOm4uLi1L17d7Vq1Urbtm3TihUrdO7cOT3wwAO297z44ov69ddf9f3332vlypVau3atduzY4VS7GRkZ6tmzpypUqKD169drw4YNCg4OVq9evexGHKxZs0ZHjx7VmjVr9Mknn2j+/Pl2Xxw89thjWrhwoT744APt379fs2bNUnBwsEwmk4YOHap58+bZtTtv3jx17txZDRs2zDWu9evXq1GjRqpQoYJT2yNJoaGhdqMpdu7cqWrVqqlz5862MvXq1VOtWrW0fv16p+sHAMDdfD0dAAAAcN6PP/6o4OBg2/PevXvrq6++kiRdf/31evfdd22vvfnmm2rVqpUmTJhgWzd37lyFhobq0KFDqlOnjj7++GN9/vnnuu222yRlfXFQt25dp2JatGiRrFar5syZI5PJJCkrIa9cubLWrl2rO+64Q5JUpUoVTZs2TWazWTfccIPuvPNORURE6IknntChQ4e0ePFirVq1Sj169JAkXXvttbY2Bg8erFdffVVbtmxR+/btlZGRoS+++CJH7/+Vjh8/rjp16uT62kMPPSSz2Wy3Li0tzTYqwGw2KyQkRJKUmpqqfv36qWPHjho/frzde+rUqaPjx487sbcAACgeJP0AAJRC3bp104wZM2zPg4KCbMtt2rSxK7tr1y6tWbPG7kuCbEePHlVKSorS09PVoUMH2/qqVauqcePGTsW0a9cuHTlyJEePempqqt3Q96ZNm9ol2rVr19Yff/whKWuovtlsVpcuXXJto06dOrrzzjs1d+5ctW/fXkuXLlVaWpruv//+PONKSUlRuXLlcn3t/ffft325kG3s2LGyWCw5yg4dOlQJCQlatWqVfHzsB0uWL19eycnJecYAAICnkPQDAFAKBQUF5Tmc/covACQpMTFRffv21TvvvJOjbO3atR2e9d9kMskwDLt1V16Ln5iYqDZt2uQ6k32NGjVsy35+fjnqzb7lXfny5QuM4/HHH9ejjz6q999/X/PmzdOAAQMUGBiYZ/nq1avbvlS4WkhISI79WKFCBcXFxdmte/PNN/Xzzz9ry5YtuV4mEBsba7eNAACUFCT9AAB4udatW+ubb75RWFiYfH1z/um/7rrr5Ofnp82bN6tevXqSpIsXL+rQoUN2Pe41atTQ2bNnbc8PHz5s17vdunVrLVq0SDVr1lTFihULFWvz5s1ltVr166+/5uiBz9anTx8FBQVpxowZWrFihdatW5dvna1atdKMGTNkGIbtsgNnfPPNN3r99de1fPlyXXfddTlezx7J0KpVK6frBgDA3ZjIDwAALzdixAjFxsbqoYce0tatW3X06FH9/PPPGjJkiCwWi4KDgzVs2DC9+OKL+uWXX7Rnzx4NHjw4xxD27t27a9q0adq5c6e2bdump59+2q7X/uGHH1b16tV1zz33aP369YqKitLatWv13HPP6dSpUw7FGhYWpkGDBmno0KFasmSJrY7FixfbypjNZg0ePFgvv/yyrr/+enXs2DHfOrt166bExETt3bvXib2WZc+ePXrsscc0duxYNW3aVNHR0YqOjlZsbKytzO+//66AgIAC4wAAwBNI+gEA8HJ16tTRhg0bZLFYdMcdd6h58+YaNWqUKleubEvsJ02apE6dOqlv377q0aOHbr311hxzA0yePFmhoaHq1KmTBg4cqDFjxtgNqw8MDNS6detUr1493Xvvvbrxxhs1bNgwpaamOtXzP2PGDN13330aPny4brjhBj3xxBNKSkqyKzNs2DClp6dryJAhBdZXrVo19e/fP9fLDgqybds2JScn680331Tt2rVtj3vvvddWZuHChXr44YfzvcQAAABPMRlXX5wHAAAgqWvXrgoPD9fUqVM9HUoO69ev12233aaTJ0+qVq1aBZbfvXu3br/9dh09ejTXCQ0L68KFC2rcuLG2bdumBg0auKxeAABchZ5+AABQaqSlpenUqVMaP3687r//focSfklq0aKF3nnnHUVFRbk0nmPHjunDDz8k4QcAlFhM5AcAAEqNhQsXatiwYQoPD9enn37q1HsHDx7s8njatm2rtm3burxeAABcheH9AAAAAAB4KYb3AwAAAADgpUj6AQAAAADwUiT9AAAAAAB4KZJ+AAAAAAC8FEk/AAAAAABeiqQfAAAAAAAvRdIPAAAAAICXIukHAAAAAMBLkfQDAAAAAOCl/h8BTEs7iei/3wAAAABJRU5ErkJggg==", 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/XHfddZd69OihDRs2ZLtd8fHx+uGHH9S5c2eVL1++qMO1cyVuZ2PO6/N0VsOGDRUXF6cXX3xRK1eu1FdffeXwD747jvG82ilJx3FgYGC2z6LOvL/M1V8oF8Trr7+ucuXK6dlnn3VLfc7yhu0r6LlhtVr17bff6s4771RYWJh9fp8+ffTDDz9o2bJluuuuu+zzC3qOuNJeSTpXnOXsMVdUx+a3336rJ598Uvv371fVqlUlSffdd59sNptefvllPfzww4Xy98SV7Suu+8Jdf5euRyT9KJGeffZZzZgxQ8OHD1ebNm0UGhoqk8mkhx56SDabzW3t5PRLJadvxK9O7J2N8cEHH7T3TC9dulTjxo3T2LFj9f3336tr1672sv/85z/Vt2/fbNu9ehSCp+Qnzmv/aOR3W535PHLjjn1cvnx5paenKy4uTqVKlbLP37FjhypXrpxlFMf27dvVsGHDLN+kb9++3aGtypUra9SoUfr666+d2pbsFCSG9PR0RURE6I8//lDVqlU1Z84c9ejRQ4cPH87S83Lp0iUFBQXl+M9AamqqLl686FTMFSpUyPZzzWk/F5YHHnhATz31lPbv36969eplWb5gwQIlJibq0UcfLfRYXJFb3M7GnNfnmSmvzyTzC7fx48fr7rvv1j//+U+H5e44xvNqpyQdx5UrV9aJEyeyrHPq1ClJynZEmKsOHDigqVOnasKECTp58qR9fnJystLS0nT48GGVLl1a5cqVK3Bb1/KG7SvoufH777/r1KlTevvttx3K33PPPQoKCtKsWbMckv6CniOutFeSzhVnOXvMFcWxKUmffvqpmjZtak9yM91zzz2aOXOmtm3bVihflriyfcV1Xzh77iErkn4UCxUqVFBgYKAOHDiQZdm+ffuyzJs3b5769u2rDz74wD4vOTnZqbts5+bAgQMOvc8HDx6UzWbL1825XImxcuXKGjx4sAYPHqyzZ8+qWbNmeuedd9S1a1dVqFBBpUqVktVqdfmPgKv7tSAKEqc768iuztKlS2vnzp2F2m5mL2t0dLRD0r5jxw41adIkS/nt27ere/fuDvPS0tK0b98+hyHXPXv2lKQCjcooSAzBwcEaOXKkfflDDz2kESNGaN++fWrevLnDutHR0br55ptzjGPt2rXq0KGDUzFHR0dne97ltJ8LS+YQxpxGEM2aNUshISFZRlF4Wm5xOxtzXp9nprw+k8wvHcqUKZPtk0PccYzn1U5JOo4jIyO1fPlyh8vFJGnDhg325QV14sQJ2Ww2DRs2TMOGDcuyvEaNGnruuedyveN9fnnD9hX03Jg1a5Z8fX3Vq1cvh/LBwcHq3r275s+fr8mTJ9sTm4KeI660V5LOFWc5e8wVxbEpZdywOHNE4tXS0tIkZXzxUhhc2b7iui+cPfeQFY/sQ7FgsVjUuXNnLViwQEePHrXP37Nnj5YsWZJt+Wt7cT/++GNZrdYCxTFx4sQsdUpS165dXa7LmRitVmuWf8orVqyoKlWq2IdVWSwW3X///fruu++yTVzPnTuXawyu7NeCKEic7qzjWmazWT179tRPP/2kzZs3Z1luGIZb2s0c+nh1G1arVbt3786ScJ8/f16nTp3KMn/Pnj1KS0tzazLr7hgOHDigixcvqnbt2lmWbd26VbfeemuOsWRe3+nMK6frO7Pbz65KTEzU3r17He7dkN3lG2lpafryyy8VGBio+vXrZ1l+7tw5/fbbb+rVq1ehX1uYXcyS63G7EnNen2emvD6Tzz77TFJGz427eocK2k5xPo4feOABWa1Wh0uNUlJSNGPGDLVu3dp+x+ycjglnNGzYUPPnz8/yatCggapXr6758+dr4MCBLtfrDG/YvoKcG0lJSfr+++911113ZZvs9OnTR3Fxcfrxxx/zHd/VCtpecT5XnOXsMedsOVddeyzXrVtX27Zt0/79+x3KffPNNzKbzYX2hbYr21cY+yK7c9rVfeHsuYes6OlHsfHmm29q8eLFuuOOOzR48GClp6fr448/VoMGDew3Ist0991366uvvlJoaKjq16+vdevW6bfffivwNVDR0dG655571KVLF61bt07/+9//9Mgjj2TbS5oXZ2KMi4tT1apV9cADD6hJkyYKCQnRb7/9pk2bNjmMEHj33Xe1fPlytW7dWoMGDVL9+vV18eJFbd26Vb/99luuw+dc2a8FVZA43VnHtUaPHq2lS5eqXbt2evLJJ3XzzTfr1KlTmjt3rv744w+VKVOmwO3WrFlTDRs21G+//aYBAwZIyvhnKTk5Ocvxk/kovGvnZ34erv7BN5lMateunVasWJFlmTtjSEpK0j//+U+9+uqrCg0NdVi2ZcsWXbx4Uffee2+Ocbrj+s7s9nOmTz75RDExMfbhvD/99JOOHz8uKeNym8yYN27cqA4dOuiNN96wXxv71FNPKTY2Vm3bttUNN9yg06dPa9asWdq7d68++OCDbG8iNXv2bKWnp+c6TD63z8aVuLOLOT9xOxOz5NznmSm3z+TQoUP6v//7P0nS3r1786wrJ3ntR1faKe7HcevWrdW7d2+9+uqrOnv2rGrXrq0vvvhChw8f1ueff24vl9Mx4czxFBYWZu89vlpmz3d2y5w9v/L6rEr69hX03Pjxxx8VFxeX4yM1u3fvrlKlSmnWrFl5Pi70ajnt94K0V9zPFWc/M2ePOWfLudr+tcfyiy++qEWLFumOO+7Q0KFDVb58ef38889atGiRnnjiiSxfWuZ1Trl7P7hatiB/e13ZF66ce8hGkT4rANcdVx7ZZxiGsXLlSqN58+aGn5+fUbNmTWPy5Mn2x7Jd7dKlS0b//v2NsLAwIyQkxOjcubOxd+9e48YbbzT69u2baxvZyWxj9+7dxgMPPGCUKlXKKFu2rDF06FAjKSkpS7lz587luS3OxJiSkmK8+OKLRpMmTYxSpUoZwcHBRpMmTbJ9jNaZM2eMIUOGGNWqVTN8fX2N8PBw4x//+IcxderUXLfNlf1a0Ef2ORtnTvvR1W115fM4cuSI8fjjjxsVKlQw/P39jZo1axpDhgwxUlJSXG43J+PHjzdCQkLsj/ebM2eOIcnYuXNnlnKSjJiYGIf5L730klG6dGn7YwSv9tRTTxlvvPFGlvlxcXGGJOOhhx7KNiZ3xZCammp0797deOSRR7KN7+WXXzaqV6+e7TJ3u3Y/Z7rxxhudehxU5vF79f785ptvjI4dOxqVKlUyfHx8jLJlyxodO3Y0fvjhhxzjuOWWW4yKFSsa6enp2S7P67NxJe7sYs5P3HnFnMnVzzO7z8Rmsxnt2rUzypYta/Tv398ICQnJtb78HuOutFMSjmPDMIykpCTjhRdeMMLDww1/f3+jZcuWxuLFix3K5HRMOHseZCe3R9o5U6+zx3xJ3T7DKPi50aNHD8Pf39/hManXevTRRw1fX98sj5DNzzmS3/ZKwrniyrHgzDHnSjlX2s/uWN6wYYPRtWtXIzw83PD19TXq1q1rvPPOO0ZaWppDG86cU4WxH1wpW5D94Mq+KMpjzhuR9ANG3kko4IyYmBijXLlyxrRp09xed07/7C1cuNAwmUzGjh073N5mJqvVavTp08e4++67s/wRNgzDSE5ONsLDw40JEyYUWgxXK8z97E5F8dkUhvx8ntl9Jp988okhyfjyyy+Nb775xpBkHDp0KMc68nuMO9sOx3HhK6nHvLPcdW7kV1H9HeBcKT68/ZxyVlEfc96Ia/oBwE1CQ0P10ksvady4cW57ikR6erqSk5NltVodpjMtX75cDz30kBo1auSW9rLz1FNP2S+H8PHJelXYjBkz5Ovrq6effrrQYrhaYeznwlAUn01hyM/nee1ncvjwYb3yyivq0aOHHnvsMfs+2Lp1a5Z1C3KMu9IOx3HhK6nHvLPccW7kR1H/HeBcKT68/ZxyVlEfc17J0986AMUBPf0orjKPzatfM2bMKLL2Dx8+bEgyAgICjODgYPtr1apVRRYDShabzWbceeedRtmyZY2TJ08ahmEYaWlpRkhIiFG3bl1jypQpRnx8vL18fo9xV9rhOEZJVpR/BzhXAO9kMgwnH2QNeLFRo0bpzTff1Llz5xQWFubpcACgxJoyZYqefvppffnll3rsscfs82fOnKl///vfOnfunOLi4uTr61si2gEAoKQj6QcAAAAAwEtxTT8AAAAAAF6KpB8AAAAAAC+V9ZaccInNZtPJkydVqlQpmUwmT4cDAAAAAPByhmEoLi5OVapUkdmce18+SX8BnTx5UtWqVfN0GAAAAACA68yxY8dUtWrVXMuQ9BdQqVKlJGXs7NKlS3s4GgAAAABAiWSzSceOZUxXqybl0oMfGxuratWq2fPR3JD0F1DmkP7SpUuT9AMAAAAA8ichQWrcOGM6Pl4KDs5zFWcuMedGfgAAAAAAeCmSfgAAAAAAvBRJPwAAAAAAXopr+ouI1WpVWlqap8NAMePn55fnIzYAAAAAIL9I+guZYRg6ffq0YmJiPB0KiiGz2awaNWrIz8/P06EAAAAA8EIk/YUsM+GvWLGigoKCnLq7Iq4PNptNJ0+e1KlTp1S9enWODQAAAABuR9JfiKxWqz3hL1++vKfDQTFUoUIFnTx5Uunp6fL19fV0OAAAAAA8xcdHGjz472l3Veu2mpBF5jX8QUFBHo4ExVXmsH6r1UrSDwAAAFzP/P2liRPdXi13ECsCDNtGTjg2AAAAABQmevoBAAAAAPA0w5DOn8+YDguT3NRBSE8/SgyTyaQFCxZ4OgwAAAAAcL/ERKlixYxXYqLbqiXpR7b69esnk8mkd99912H+ggULPDYk/dSpU+ratatH2gYAAACAkoikHzkKCAjQ2LFjdenSJU+HIkkKDw+Xv7+/p8MAAAAAgBKDpB856tixo8LDwzVmzJhcy3333Xdq0KCB/P39FRERoQ8++CDX8qNGjVJkZKSmT5+u6tWrKyQkRIMHD5bVatV7772n8PBwVaxYUe+8847DelcP7z98+LBMJpO+//57dejQQUFBQWrSpInWrVtXoG0GAAAAgGIpPVG6tF06u1q6GOX0atzIr4gZhqFEN16f4YqgoCCXhuZbLBaNHj1ajzzyiIYNG6aqVatmKbNlyxY9+OCDGjVqlPr06aO1a9dq8ODBKl++vPr165dj3YcOHdKiRYu0ePFiHTp0SA888ID++usv1a1bVytXrtTatWs1YMAAdezYUa1bt86xntdee03vv/++6tSpo9dee00PP/ywDh48KB83PtcSAAAAADwm5YJ0/Efp4ibJsGbMi091enUyoyKWmJiokJAQj7QdHx+v4OBgl9bp1auXIiMj9cYbb+jzzz/Psnz8+PH6xz/+oX//+9+SpLp162r37t0aN25crkm/zWbT9OnTVapUKdWvX18dOnTQvn379Msvv8hsNqtevXoaO3asli9fnmvS/8ILL6h79+6SpDfffFMNGjTQwYMHddNNN7m0nQAAAABQrBiGdHaldHSeZKRlzAuoLAVUkIwzTlfD8H7kaezYsfriiy+0Z8+eLMv27Nmj2267zWHebbfdpgMHDshqteZYZ0REhEqVKmV/X6lSJdWvX19ms9lh3tmzZ3ONrXHjxvbpypUrS1Ke6wAAAABAsZaeIB34VDryTUbCX6quVP9VqfEoqe4Q6eYXnK6Knv4iFhQUpPj4eI+1nR9t27ZV586d9eqrr+bae+8KX19fh/cmkynbeTabzel6Mi9dyGsdAAAAACh2fHykvn0la5K0f7xkXJJMPlK1+6RKd0r5fIoaSX8RM5lMLg+xLw7effddRUZGql69eg7zb775Zq1Zs8Zh3po1a1S3bl1ZLJaiDBEAAAAASi5/f2nCi9LBqZLtkuRXXqo7WArKem81V5D0wymNGjXSo48+qo8++shh/vPPP6+WLVvqP//5j/r06aN169bpk08+0aeffuqhSAEAAACgBLq4RTo4TZJNKlVHqv2U5Fsqz9XywjX9cNpbb72VZeh8s2bNNGfOHH377bdq2LChRo4cqbfeesttlwEAAAAAgNc7t046MFVKSpECm0h1n3NLwi9JJsMwDLfUdJ2KjY1VaGioLl++rNKlSzssS05OVnR0tGrUqKGAgAAPRYjijGMEAAAAuM5d2CwdmiYlpUrtZ2TMi4+XcrksPLc89Fr09AMAAAAA4AmX90p/zZBkSGG35Vk8P0j6AQAAAAAoakmnpQOTJCNdKttMuvHBQmmGpB8AAAAAgKJkTZEOTJZsyRk37as1QDIVTnpO0g8AAAAAQFE6/D8p+ZTkW0aq/aRk9i20pkj6AQAAAAAoKhe3SBc2SjJnJPy+ud+Ir6BI+gEAAAAAKAppcdLhbzKmq3SRStUq9CZ9Cr0FAAAAAAAgHftOSo+TAqtIVbo7LrNYpAce+HvaTUj6AQAAAAAobAlHpfPrMqZrPC6Zr0nHAwKkuXPd3izD+wEAAAAAKEyGIR29ktCXbyWF1Ciypkn64TYrVqyQyWRSTExMruUiIiI0YcKEYhMPAAAAABSq2H1S3H7J5CNV7VmkTZP0I4vJkyerVKlSSk9Pt8+Lj4+Xr6+v2rdv71A2M7E+dOiQbr31Vp06dUqhoaGSpJkzZ6pMmTJFGLmja+MBAAAAAI84tSTjZ4XbJf/y2ZdJSJBMpoxXQoLbmibpRxYdOnRQfHy8Nm/ebJ+3evVqhYeHa8OGDUpOTrbPX758uapXr65atWrJz89P4eHhMplMngg7i+IWDwAAAIDrUMIxKXa3JLMU3qnImyfpRxb16tVT5cqVtWLFCvu8FStW6N5771WNGjW0fv16h/kdOnSwT2cOp1+xYoX69++vy5cvy2QyyWQyadSoUfb1EhMTNWDAAJUqVUrVq1fX1KlTc42pffv2evbZZzV8+HCVLVtWlSpV0meffaaEhAT1799fpUqVUu3atbVo0SKH2K4e3p858mDJkiW6+eabFRISoi5duujUqVMF32kAAAAAkJ2zKzN+lmsmBYQVefMk/UXNMCRrimdehuF0mB06dNDy5cvt75cvX6727durXbt29vlJSUnasGGDPem/2q233qoJEyaodOnSOnXqlE6dOqUXXnjBvvyDDz5QixYttG3bNg0ePFjPPPOM9u3bl2tMX3zxhcLCwrRx40Y9++yzeuaZZ9S7d2/deuut2rp1q+666y499thjSkxMzLGOxMREvf/++/rqq6+0atUqHT161CEuAAAAAHAba4p0YVPGdMV2HgmBR/YVNVuqtGWYZ9pu/pFk8XeqaIcOHTR8+HClp6crKSlJ27ZtU7t27ZSWlqbJkydLktatW6eUlJRsk34/Pz+FhobKZDIpPDw8y/Ju3bpp8ODBkqSXX35ZH374oZYvX6569erlGFOTJk30+uuvS5JeffVVvfvuuwoLC9OgQYMkSSNHjtSkSZO0Y8cO3XLLLdnWkRl/rVq1JElDhw7VW2+95dQ+AQAAAACXXNwq2ZIl/zCpVB2PhEDSj2y1b99eCQkJ2rRpky5duqS6deuqQoUKateunfr376/k5GStWLFCNWvWVPXq1V2uv3HjxvbpzC8Gzp496/Q6FotF5cuXV6NGjezzKlWqJEm51hMUFGRP+CWpcuXKebYLAAAAAPlyYWPGz7BbM27Q5wEk/UXN7JfR4+6ptp1Uu3ZtVa1aVcuXL9elS5fUrl3GUJQqVaqoWrVqWrt2rZYvX64777wzX6H4+vo6vDeZTLLZbC6vc/W8zBv25VZPdnUYLlz2AAAAAABOSU+S4q5cwlyuucfCIOkvaiaT00PsPa1Dhw5asWKFLl26pBdffNE+v23btlq0aJE2btyoZ555Jsf1/fz8ZLVaiyJUAAAAACheLu+SDKsUUEkKzHrJcxYWi9St29/TbsKN/CT9/PPPqlevnurUqaNp06Z5Opxio0OHDvrjjz8UFRVl7+mXpHbt2mnKlClKTU3N9nr+TBEREYqPj9eyZct0/vz5XG+wBwAAAABeJWZ7xs+ykc6VDwiQFi7MeAUEuC2M6z7pT09P14gRI/T7779r27ZtGjdunC5cuODpsIqFDh06KCkpSbVr17ZfLy9lJP1xcXH2R/vl5NZbb9XTTz+tPn36qEKFCnrvvfeKImwAAAAA8CzDkGL3ZkyHNvRoKCbjOr+gee3atRo3bpzmz58vSRo+fLhat26thx9+2Kn1Y2NjFRoaqsuXL6t06dIOy5KTkxUdHa0aNWoowI3f1MB7cIwAAAAAXijpjPTnSMnkIzWfIJl981zFFbnlodcq8T39q1atUo8ePVSlShWZTCYtWLAgS5mJEycqIiJCAQEBat26tTZu3GhfdvLkSd1www329zfccINOnDhRFKEDAAAAALxR3IGMnyE1nE/4ExKk4OCMV0KC20Ip8Ul/QkKCmjRpookTJ2a7fPbs2RoxYoTeeOMNbd26VU2aNFHnzp15TBsAAAAAoHBkJv2l6ri2XmJixsuNSnzS37VrV7399tvq1atXtsvHjx+vQYMGqX///qpfv74mT56soKAgTZ8+XVLGI+iu7tk/ceKEqlSpkmN7KSkpio2NdXgBAAAAAGCXeDTjZ3ANz8YhL0j6c5OamqotW7aoY8eO9nlms1kdO3bUunXrJEmtWrXSzp07deLECcXHx2vRokXq3LlzjnWOGTNGoaGh9le1atUKfTsAAAAAACWELU1KOp0xHez5fNGrk/7z58/LarU63HlekipVqqTTpzM+BB8fH33wwQfq0KGDIiMj9fzzz6t8+fI51vnqq6/q8uXL9texY8cKdRsAAAAAACVI0ilJNskSLPmW8XQ08vF0AMXBPffco3vuucepsv7+/vL39y/kiAAAAAAAJVLi8YyfwdUkk8mzscjLe/rDwsJksVh05swZh/lnzpxReHi4h6ICAAAAAHitpFMZPwNvyL1cEfHqpN/Pz0/NmzfXsmXL7PNsNpuWLVumNm3aeDAyAAAAAIBXSjmX8TOggmvrmc1Su3YZL7P7UvUSP7w/Pj5eBw8etL+Pjo5WVFSUypUrp+rVq2vEiBHq27evWrRooVatWmnChAlKSEhQ//79PRg1AAAAAMArJV9J+v1dTPoDA6UVK9weTonv6d+8ebOaNm2qpk2bSpJGjBihpk2bauTIkZKkPn366P3339fIkSMVGRmpqKgoLV68OMvN/eCamTNnqkyZMkXa5qhRoxQZGVmkbQIAAACA0wzj755+V5P+QlLik/727dvLMIwsr5kzZ9rLDB06VEeOHFFKSoo2bNig1q1bey7gEqJfv34ymUwymUzy8/NT7dq19dZbbyk9Pd3TobnEZDJpwYIFng4DAAAAwPUgPU6ypUgySf45PxWuKJX4pB+Fp0uXLjp16pQOHDig559/XqNGjdK4ceM8HVaJlZqa6ukQAAAAABSmlAsZP/3KSGYXr6ZPSJAqVMh4JSS4LSSSfuTI399f4eHhuvHGG/XMM8+oY8eO+vHHH7Mte+jQId17772qVKmSQkJC1LJlS/32228OZSIiIjR69GgNGDBApUqVUvXq1TV16lSHMsePH9fDDz+scuXKKTg4WC1atNCGDRtybLNmzZoaOnSoDMPIsjwiIkKS1KtXL5lMJvt7Z2P9z3/+o4cffljBwcG64YYbNHHiRIcyMTExeuKJJ1ShQgWVLl1ad955p7Zv325fnnk5wrRp01SjRg0FBARkux0AAAAAvETa5YyfvmXyt/758xkvNyLp95SEhJxfycnOl01Kcq6sGwQGBubYWx0fH69u3bpp2bJl2rZtm7p06aIePXro6NGjDuU++OADtWjRQtu2bdPgwYP1zDPPaN++ffY62rVrpxMnTujHH3/U9u3b9dJLL8lms2Vpb8eOHbr99tv1yCOP6JNPPpEpm+dfbtq0SZI0Y8YMnTp1yv7e2VjHjRunJk2aaNu2bXrllVf03HPP6ddff7Uv7927t86ePatFixZpy5Ytatasmf7xj3/o4sWL9jIHDx7Ud999p++//15RUVFO7GUAAAAAJVZabMZP39KejeMqJf7u/SVWSEjOy7p1kxYu/Pt9xYpSYmL2Zdu1c7zDY0RE9t8MZdMT7izDMLRs2TItWbJEzz77bLZlmjRpoiZNmtjf/+c//9H8+fP1448/aujQofb53bp10+DBgyVJL7/8sj788EMtX75c9erV09dff61z585p06ZNKleunCSpdu3aWdpau3at7r77br322mt6/vnnc4y7QoWMG2eUKVNG4eHhLsd622236ZVXXpEk1a1bV2vWrNGHH36oTp066Y8//tDGjRt19uxZ+fv7S5Lef/99LViwQPPmzdOTTz4pKWNI/5dffmmPBQAAAIAXS83s6Q/1bBxXIelHjn7++WeFhIQoLS1NNptNjzzyiEaNGpVt2fj4eI0aNUoLFy7UqVOnlJ6erqSkpCy9540bN7ZPm0wmhYeH6+zZs5KkqKgoNW3a1J7wZ+fo0aPq1KmT3nnnHQ0fPjxf2+VsrG3atMnyfsKECZKk7du3Kz4+XuXLO96cIykpSYcOHbK/v/HGG0n4AQAAgOsFPf2wi4/PeZnF4vj+SlKcLfM1V2gcPpzvkK7VoUMHTZo0SX5+fqpSpYp8fHI+XF544QX9+uuvev/991W7dm0FBgbqgQceyHI5gK+vr8N7k8lkH74fGBiYZ0wVKlRQlSpV9M0332jAgAEqXdr1k8nZWHMTHx+vypUra0U2z9G8+lGGwcHBLscHAAAAoITKvKbfj55+uJIMFlbZPKsKznZ4fXbWrFmjfv36qVevXpIykuLDLn4B0bhxY02bNk0XL17Msbc/MDBQP//8s7p166bOnTtr6dKlKlWqVI51+vr6ymq15ivW9evXZ3l/8803S5KaNWum06dPy8fHx36DQAAAAADXufS4jJ8+OecoRY0b+cEt6tSpY79Z3fbt2/XII49kewO+3Dz88MMKDw9Xz549tWbNGv3111/67rvvtG7dOodywcHBWrhwoXx8fNS1a1fF5zJqIiIiQsuWLdPp06d16dIll2Jds2aN3nvvPe3fv18TJ07U3Llz9dxzz0mSOnbsqDZt2qhnz55aunSpDh8+rLVr1+q1117T5s2bXdpuAAAAAF4i/cpN1H3y0RlrNkstWmS8rh3RXQAk/XCL8ePHq2zZsrr11lvVo0cPde7cWc2aNXOpDj8/Py1dulQVK1ZUt27d1KhRI7377ruyXHu5g6SQkBAtWrRIhmGoe/fuSsjhCQUffPCBfv31V1WrVk1NmzZ1Kdbnn39emzdvVtOmTfX2229r/Pjx6ty5s6SMyxJ++eUXtW3bVv3791fdunX10EMP6ciRI6pUqZJL2w0AAADAS1ivPF3Nkvely1kEBkqbNmW8nLj02VkmI7sHnMNpsbGxCg0N1eXLl7NcX56cnKzo6Gie0V4CRUREaPjw4fm+WaCzOEYAAAAAL7JpqGSkSY3fkQLCCq2Z3PLQa9HTDwAAAABAQdnSMxJ+SfIJ8mwsVyHpBwAAAACgoDKH9kuSJR+jeBMTpYiIjFdiorui4u79QHZcffIAAAAAgOtcZtJv9pdM+ehfNwzpyJG/p92Enn4AAAAAAAqqIDfxK0Qk/UWAeyUiJxwbAAAAgJdIJ+m/7vj6+kqSEt14PQa8S2pqqiRl+1hCAAAAACWILeN/e1n8PBvHNbimvxBZLBaVKVNGZ8+elSQFBQXJZDJ5OCoUFzabTefOnVNQUJB8fDgVAQAAgBLNSM/4aSbpv66Eh4dLkj3xB65mNptVvXp1vgwCAAAASjrblcf1mYpXml28ovFCJpNJlStXVsWKFZWWlubpcFDM+Pn5yWzmKhsAAACgxMsc3p/fnn6TSapf/+9pNyHpLyIWi4XrtgEAAADAW9mH9/vmb/2gIGnXLvfFcwVdjAAAAAAAFFQxHd5P0g8AAAAAQEEVdHh/ISHpBwAAAACgoGwFHN6fmCg1aJDxcuNj34vXuAMAAAAAAEoie09/PpN+w5B27/572k3o6QcAAAAAoKAyr+nPb9JfSEj6AQAAAAAoqMy793MjPwAAAAAAvIxhy/hpKl6PaifpBwAAAACgoAxrxk9T8Uqzi1c0AAAAAACUSFduvlfMkv7idbEBAAAAAAAlUWZPf3771k0m6cYb/552E5J+AAAAAAAKyn5Nfz6T/qAg6fBht4WTqXiNOwAAAAAAoCQqaNJfSIpXNAAAAAAAlEhXkv5ilmYXr2gAAAAAACiJCtrTn5QktWyZ8UpKcltYXNMPAAAAAEBBFTTpt9mkzZv/nnYTevoBAAAAACgwrukHAAAAAMA7GVzTDwAAAACAd+Lu/QAAAAAAeCuSfgAAAAAAvFMxHd7P3fsBAAAAACgodwzvDwtzTyxXIekHAAAAAKCgCpr0BwdL5865L54rite4AwAAAAAASqTiOby/eEUDAAAAAEBJxN37AQAAAABAtpKSpPbtM15JSW6rlmv6AQAAAADwNJtNWrny72k3oacfAAAAAAAvRdIPAAAAAICXIukHAAAAAMBLkfQDAAAAAOClSPoBAAAAAPBS3L0fAAAAAIDiICjI7VWS9AMAAAAA4GnBwVJCgturZXg/AAAAAABeiqQfAAAAAAAvRdIPAAAAAECBGQVbPTlZ6t4945Wc7J6QRNKvY8eOqX379qpfv74aN26suXPnejokAAAAAEBJZTLlbz2rVfrll4yX1eq2cK77G/n5+PhowoQJioyM1OnTp9W8eXN169ZNwcHBng4NAAAAAIACue6T/sqVK6ty5cqSpPDwcIWFhenixYsk/QAAAACAEq/YD+9ftWqVevTooSpVqshkMmnBggVZykycOFEREREKCAhQ69attXHjxny1tWXLFlmtVlWrVq2AUQMAAAAA4HnFPulPSEhQkyZNNHHixGyXz549WyNGjNAbb7yhrVu3qkmTJurcubPOnj1rLxMZGamGDRtmeZ08edJe5uLFi3r88cc1derUQt8mAAAAAACKQrEf3t+1a1d17do1x+Xjx4/XoEGD1L9/f0nS5MmTtXDhQk2fPl2vvPKKJCkqKirXNlJSUtSzZ0+98soruvXWW/Msm5KSYn8fGxvr5JYAAAAAAFC0in1Pf25SU1O1ZcsWdezY0T7PbDarY8eOWrdunVN1GIahfv366c4779Rjjz2WZ/kxY8YoNDTU/uJSAAAAAABAcVWik/7z58/LarWqUqVKDvMrVaqk06dPO1XHmjVrNHv2bC1YsECRkZGKjIzUn3/+mWP5V199VZcvX7a/jh07VqBtAAAAAABAwcGSYWS83Hhj+WI/vL+w3X777bLZbE6X9/f3l7+/fyFGBAAAAACAe5Tonv6wsDBZLBadOXPGYf6ZM2cUHh7uoagAAAAAACgeSnTS7+fnp+bNm2vZsmX2eTabTcuWLVObNm08GBkAAAAAAC5ITpZ69854JSe7rdpiP7w/Pj5eBw8etL+Pjo5WVFSUypUrp+rVq2vEiBHq27evWrRooVatWmnChAlKSEiw380fAAAAAIBiz2qV5s3LmJ45M9eiX3/9tdPVFvukf/PmzerQoYP9/YgRIyRJffv21cyZM9WnTx+dO3dOI0eO1OnTpxUZGanFixdnubkfAAAAAADe4JlnnnG6rMkwDKMQY/F6sbGxCg0N1eXLl1W6dGlPhwMAAAAA8ITtr0kp56X6L0shNV1fPyFBCgnJmI6Pz/UO/iaTSZKcykNL9DX9AAAAAAAgZyT9AAAAAAB4KZJ+AAAAAAC8FEk/AAAAAABeqtjfvR8AAAAAAK8XFJRxA7/MaTch6QcAAAAAwNNMplzv2J9fDO8HAAAAAKCYWrRokUwmk/3l4+Na3z09/QAAAAAAeFpKivTUU5Kk33r3Vqe77862mNVqdalakn4AAAAAADxs49q1avXFF5Kke6/8dAeSfgAAAAAA3MbkdMmTJ0/qhhtukCQFSUoohGi4ph8AAAAAgIIyDKeLrl+/XiaTyZ7wFyZ6+gEAAAAAKGTHjx9XtWrVirxdevoBAAAAACgkH374oUwmk0cSfomefgAAAAAA3MYwDE369FMNGTLE06FIIukHAAAAAMAtVqxYof53T9Xhc56O5G8k/QAAAAAA5NO5k39p7pvNZUmPkSSZnb95v4NESRWumnYXrukHAAAAACAf0lKT9f2/a9kTfkm6MSz/9Z2/8nInkn4AAAAAAPLh8N6NWeZZilmWXczCAQAAAACg5Mrv8H4/SZ9cefm5Mx431gUAAAAAwHXNnM8s20fSkCsvd958j6QfAAAAAAA3yWdHf6Eh6QcAAAAAwE3Ox3k6Akck/QAAAAAAuEm6zdMROCLpBwAAAAAgHwzD8HQIeSLpBwAAAADAS5H0AwAAAADgpdz5JAAAAAAAALze8b/+1MJ3Gru1ziRJEVdNuwtJPwAAAAAAeTi4c42WfXh7odVvSDpSCPWS9AMAAAAAkIMj+7do8dgWng4j30j6AQAAAAC4imGz6buJz+hC1NQia9NX0jtXpl+TlOamekn6AQAAAAC44sCOVfr9v+2KvF1fSS9emR4lkn4AAAAAANzCmp6mKSNuliXhkKdDcTuSfgAAAADAdSklKUEzh4ZIkiwejqWwkPQDAAAAAK4bxw5G6cyxvdryv4c9HUqRIOkHAAAAAFwXvvjP3Uo+vNDTYRQpkn4AAAAAgFfbs/U3rZrYydNheARJPwAAAADA6xg2m36e+apOrnnP06F4VL6S/ho1ashkMrm83vDhwzVs2LD8NAkAAAAAQJ6i/pivcyf3668lr3g6FJckSWpw1bS75CvpnzlzZr4ai4iIyNd6AAAAAADkxrDZNHVQyb0HvyFpdyHUm6+kv127du6OAwAAAAAAl505fkA/fPK4jHPrPR1KsZSvpD8tLU0rVqxQQECA6tevr/Lly7s7LgAAAAAAcrVuyQztmDPA02G4ha+k/7syPVpSmpvqzVfSf99996ly5cr6/vvvVaZMGSUlJalRo0ZavHixm8ICAAAAACB7syf0V8yfMz0dhlv5Shp1ZXqcPJz0Hz16VD/99JM2btyoqKgoTZw4UUeOHHFTSAAAAAAA5MzbEv7ClK+kPyAgQJLk5+en1NRUDRkyRLfeeqtbAwMAAAAAAAWTr6R/2LBhunjxou6//349/fTTuu2223T+/Hl3xwYAAAAAAArAnJ+VHn30UZUrV04vv/yy2rZtq71792revHnujg0AAAAAABRAvnr6r9avXz83hAEAAAAAANzN5Z7+S5cu6eLFi5Kkc+fO6fvvv9euXbvcHhgAAAAAACgYl5L+adOmqXnz5mrRooUmTZqkXr16admyZXrooYc0bdq0wooRAAAAAACvliyp5ZVXshvrdWl4/0cffaRdu3YpKSlJ1atXV3R0tCpUqKDLly+rXbt2euKJJ9wYGgAAAAAA1webpM2FUK9LSb/FYlFgYKACAwNVu3ZtVahQQZIUGhoqk8lUCOEBAAAAAID8cml4v4+Pj5KTMwYarFy50j4/Pj7evVEBAAAAAHAVw2bTwi9e05SB3tnh7CvphSsvXzfW61JP/3333Sd/f39JGb37mRITEzV16lQ3hgUAAAAAgHRk/xYtHtvC02EUOl9J465MfyopzU31upT0z507V6+++mqW+RUrVlTFihXdFBIAAAAA4Hp2eN9mxV8+pw0/jlX6qZV5r4AcuZT0AwAAAABQmC6ePaYl77X0dBhew6Wkf9euXWrTpo0aNGigBg0aqGHDhmrQoIGqVKlSWPEBAAAAALxYelqq9kX9rnPH9+nS2b90duNHng7Jq7iU9NerV09Tp07Vrl27tHPnTn366afauXOnYmJidNNNN2n16tWFFScAAAAAwIukJidq77ZlWv3VU/JJOeXpcLyWy4/sa9SokRo1auQwPzk5WXv27HFrYEUtMTFRN998s3r37q3333/f0+EAAAAAgFdKTU7Usnnv6ejyNyVxzXlhc2n/Pv3009nODwgIUNOmTd0SkKe88847uuWWWzwdBgAAAAB4pT1bf9Mfc0fKdnadp0MpVAHufN6eG5hdKVyzZk3dcsstateunX777TdJ0qlTpzRjxgw9/PDDhRJgUThw4ID27t2rrl27ejoUAAAAAPA663/9QqsmdvL6hF+SqpbL33rJktpfeSW7LRoXk/5nn31Wzz//vMaPH6/58+erf//+qlevnn777Tfdc889bgzrb6tWrVKPHj1UpUoVmUwmLViwIEuZiRMnKiIiQgEBAWrdurU2btzoUhsvvPCCxowZ46aIAQAAAACS9NXoXpr4VFlt/7afp0Mp9mySVl552dxYr0vD+wMDA9W7d29JUmRkpCpUqKDdu3eratWqbgzJUUJCgpo0aaIBAwbovvvuy7J89uzZGjFihCZPnqzWrVtrwoQJ6ty5s/bt26eKFSvaY01PT8+y7tKlS7Vp0ybVrVtXdevW1dq1awttOwAAAADgemCzWpWelqIZQ4Ilcc2+p7m0/8+dO6fZs2erTp06qlu3riIiIgo14Zekrl275jrsfvz48Ro0aJD69+8vSZo8ebIWLlyo6dOn65VXXpEkRUVF5bj++vXr9e2332ru3LmKj49XWlqaSpcurZEjR2ZbPiUlRSkpKfb3sbGx+dgqAAAAAPAue7b+plUTO3k6jBLLR9KTV6anSsrabZ0/JsMwDGcLf/jhh9q5c6d27typPXv2KCUlRV26dFFkZKQiIyPVq1cvN4WVPZPJpPnz56tnz56SpNTUVAUFBWnevHn2eZLUt29fxcTE6IcffnCp/pkzZ2rnzp253r1/1KhRevPNN7PMv3z5skqXLu1SewAAAABQkpw/fVhh4REO89YtmaHzx/fo5NpxngmqmFm2S5q7wfX1giQlXJkOlpToxDrO5KEu9fT/61//cngfHR1t/xLgu+++K/Sk/1rnz5+X1WpVpUqVHOZXqlRJe/fuLZQ2X331VY0YMcL+PjY2VtWqVSuUtgAAAACguJj5VjelHFkkSbrtqV90+vCfOrTkZQ9HhbwU6PKKGjVqqEaNGurRo4e74vGofv365VnG399f/v7+hR8MAAAAAHhYanKi1i76TJFte9sTfklaM6WbB6OCK1xK+itVqqS6deuqUaNGatiwof1n2bJl7WUmTZqkZ555xu2BZicsLEwWi0VnzpxxmH/mzBmFh4cXSQwAAAAA4K2+Hf+okg4t0L6fh3s6FOSTS4/sO3nypKZMmaL27dvr9OnTGj9+vG655RZVr17dfrO9zz77rFACzY6fn5+aN2+uZcuW2efZbDYtW7ZMbdq0KbI4AAAAAMBbfDW6l6aP/IfWLZmhpEMLPB0OCsilnn6LxaL69eurfv36evDBB7Vu3TotWrRI8+fP14ULFyRJLtwX0Cnx8fE6ePCg/X10dLSioqJUrlw5Va9eXSNGjFDfvn3VokULtWrVShMmTFBCQoL9bv4AAAAAAOcs/36CEq8k+jvm/O7ZYOAWLiX958+f15IlS7Rw4UJt27ZNzZs3V5cuXfT777+rQoUKkqTdu3erTZs2atCggRo0aKCGDRuqQYMGqlKlSr4C3Lx5szp06GB/n3kTvb59+2rmzJnq06ePzp07p5EjR+r06dOKjIzU4sWLs9zcDwAAAACQu/0L/5V3IZQoLj2yz2KxqEmTJnrhhRfUp08fWSyWLGUaN26sWbNmadeuXdq5c6f9Z0xMjG666SatXr3arRvgabGxsQoNDeWRfQAAAABKtMsXT+vbFyt7OowSL7+P7LNI6nxleokkqxPruP2RfePGjdOuXbv03//+V//6179UrVo1NWzY0P7q0qWLLBaLGjVqpEaNGjmsm5ycrD179rjSHAAAAACgiBzem49MFW5jlfRLIdTrUtJ/9fPppYzr6zN78v/3v/+pS5cuevrpp7NdNyAgQE2bNs1/pAAAAACAQrPmm38p61hulHQuJf3XqlGjhho0aKDk5GRVqlRJ3bp1U9OmTbVixQq1b9/eTSECAAAAAArLrk2Lterrf8mSGO3pUK5rPpIevTI9S1K6m+p16ZF92YmLi9OOHTv0wQcfqGHDhtqwYYO6dOmiDh06KCEhwR0xAgAAAADczLDZ9NPnL+mPyV1ljt3r6XCue36SZl55+bmxXpdu5Jeds2fPqmLFilnm3Xfffbr99tv17rvvFijA4o4b+QEAAAAoiSY+XUE+aec9HYbXye+N/IIkZXabB0tKdGIdZ/LQAif9ZrNZ4eHhatKkif0VGRmphIQEdevWTWfPni1I9cUeST8AAACAkuSbD/6p2N2zPB2G1ypuSX+BrumXpAMHDmj79u3asWOHtm/frjlz5ujw4cPy8/NTWlqa/vnPf6p169aKjIzUHXfcUdDmAAAAAAAuOnpgm1bPH6t2D7xGwn+dKXBPf3ZiY2O1fPly9erVSw8//LD27dun3bt3KzHRme8qShZ6+gEAAAAUdxMHlZaPLc7TYVwXvK6nv1y5coqMjLQP7W/UqJFCQkL0yy+/qFatWpo1K+NbJKvVWtCmAAAAAAAumDHqLqUe+7XgiR9KrAJ/9tOnT9f27du1fft2/fDDDzp8+LAkKSgoSHPmzLGXs1h44iMAAAAAuNuFM0e1ftEUte05XKXKVJA1PU2Th1aWT9oFT4eGYsDtw/vj4uJ06tQp3XDDDQoODnZn1cUSw/sBAAAAeNKnQ2+UJemoVL65bnvgP1ozpZunQ7qu5Xd4v0VSryvT8yU5M1bemTzU7HoouStVqpTq1q2r4OBg7dy5093VAwAAAMB1a92SGZoy0KQ/fp5in2dJOpoxcWELCX8JZpU078rLnRfHuz3pj4uL09SpU9WqVStFRka6u3oAAAAAuO7YrFZdOHNUO+YMkCTtmv+0JGnRV294MiyUAG67n8OqVav0+eef67vvvlNQUJDuuOMObdmyxV3VAwAAAMB16c/1P2vtZz2yzJ84uIp8Uk55ICIUhvwM73dGgXr6T58+rXfffVd16tRRt27dlJ6erjlz5ujkyZN688033RQiAAAAAHi3xPgYTRxcVVMGmjRj1F06/tefslmtOhG9M9uEXxIJfzEVn5y/9fwlzb3y8ndjPPnu6e/Ro4eWLVumDh06aNSoUerZs6fDjftMJpNbAgQAAAAAbxa9Z4N++e/d8kk7L0lKPfarFr7T2MNRIb9iEj0dgaN8J/0LFy7UI488ouHDh6tFixbujAkAAAAAvNrSb0fr6I5FenzkEi19/xb3XXcNXCPfw/vXrl2rwMBA3XnnnapXr57eeustHTp0yJ2xAQAAAECJFBdzzj5t2GxZ3kf/+pqsZ/7QjCHe/5hzeJbJMAyjIBUkJCRo9uzZmj59utatW6eWLVvq0UcfVYMGDdSpUydZre582EDxExsbq9DQUKeejwgAAADAexk2mw7u/ENH92/UwUUvKqReHz360rea9uptsp5dq0YPTFN6eop2/jBCFiPF0+GikHz5h7R2v+vrBUlKuDIdLMmZqwScyUMLnPRfbd++ffr888/11Vdf6cyZMzKZTCT9AAAAAK4LX7//iOL2fOMw76nPDU0ZyP3OrifFLekv0N37r1WvXj299957On78uL7//nt1797dndUDAAAAQLFzcOcaffn2PVkSfkmaPKKhByIC/lYo94uwWCzq2bOnevbsWRjVAwAAAIDHpaUm68COVVo9qXOOZUyXdxVhRCjJUiX1u2raXfKV9NeoUSNfj+QbPny4hg0blp8mAQAAAMDj0tNSlRgfI4vFRzP/r5l8ko54OiR4iXRJXxRCvflK+mfOnJmvxiIiIvK1HgAAAAAUB1OGVJSP9bKkQho2DbhZvo7Tdu3auTsOAAAAACi2khPjNP3lxvaEH3A3i6TMC0WWSHLXLfH5cgoAAAAArmHYbJo0vK4CytyoBm37Keqbx2XxdFDwav6SFl6Zdvbu/c4g6QcAAABwXUiMj1FQSBkZNptiLp5SaNlwpaelyGzxkY+vnyQpNTlRaanJWvXjR7IkHFJawiFFffO7hyMH8o+kHwAAAIDX+37SUJ3bPFE1Or6tg+tmypJwUFZzoGTYZJj9NXjyJaWlJmvGkGBPh4oSzvVb3hcukn4AAAAAXiMtNVmL//eGGt3WWxH1Wtjnn9s8UZIU/dvr9mH6FltSxoQ1RVMHMXgf3sns6QAAAAAAwF3mTxqik2ve05L3WtrnJSfGeTAiwLPo6QcAAADgNS78+ZW9Z3PKwOI20BooeiT9AAAAAEqcFfP/q8M7Fuuf//eDfHz99NPnL+nU/hUyG2meDg3XueRidgiS9AMAAAAodnZuXKStv05W7Wb3aM+ar5Qaf0YyST2Hf6/KN96sfT8PlyQt+vJ19Rj4nk6uHefZgIEr0qz5Wy9V0pCrpt3FZBiG4cb6rjuxsbEKDQ3V5cuXVbp0aU+HAwAAAJQYNqtVi74aqbrNOqtO47aSJMNm0+JZb+roireyXSfdL1yWwDCZLu/8ux6TLz38KDYm/ir9eaxo2nImD6WnHwAAAEChMGw27d+xUhH1Wsk/8O9H4Z07+ZdSUxK1ffVsHV89WsdXj9aa6l3Uouswrf76XzLH7cuxTp/U01LqaYd5JPxAzkj6AQAAABSKX+e8q+hfX5M1KEKDP462z//+37WylE05ulhrpizm8WK4bpkl3XFlerUkm5vqJekHAAAAkG82q1WTR9SXX3BFWc/8IUmq1Po5ndj6P/mkXZAkWRIPa8pAk9L9KkoiCQGyEyBpxZXpYEmJbqqX8w0AAABArqzpaVr6zdu6uVUPRdRrIUnav32lls14Sr5BYbLE75c1fr+9/JkN/8020fBJPVtEEQPIxOgZAAAAALn6ecYrOrriLS15r6V93vKP2ssct0/WM2s8GBmAvNDTDwAAAFyHbFarPnultYLKReixV+fJsNn02f/dJv/gMPX99086djBKv4xpmmW9KQNNHogWQH6R9AMAAABexpqeJouPb47T1vQ0bVv9nXRxixIvbpE1PU37t6+QcW69ks9JqcmJ+um/98viyY0A4BYk/QAAAIAXObJ/i355r41CanZXSsJ5pZ3drAff3qetK2bp0K+vy2YpLZM1UTe2fcm+zrSn/BzqmDEkmIQf8BJc0w8AAACUIIbNlu10pl9nDJPZSFPioQWynv5DZluyfp/zH/219P9kMmyypMfIbKQqeu3kogwbwFV+/fVX1axZs0jaoqcfAAAAKCFWLPhIuxe+osj7P9alM3/pyB8TdMcTc9SodXd7GevZtVnWuxg1Lcs8n7TzhRorgOzZbDaZTCYdPHhQZvPf/fBpkl68ajo7c+bM0bZt2zRmzBin2yPpBwAAAIqZ6L0bteiTB1W/4wi17znMPn/fT8/JIunPuU9IkiyS1k69W2unZiwvffMjRR8sAJeYTCaHn5nSJL3vxLrXrpcXkn4AAACgmLBZrZr8dGlZbImyKCPJT0mMVedHXndq/dg9XxdugABKHJJ+AAAAoBAkJ8bpwI6Vatiqm9b/+oWCS4epWp1mOnt8v+pFdpAk7d32u0qXC9fFM0fUoEVnzfvkSVlsiQ71HF72b22rdrPiLp32xGYAKCJmSc2uTG+VlPWOHflD0g8AAAAUgumvNJUl4ZD++KaOzPEHJEnrriyzDV6q9LQUrf2sh7382WPv69KO6dnWtXHmA4UdLgAPC5C06cp0sKTEXMq6gqQfAAAAKASWhEOSZE/4r7bq07uU7lPO4Z/xA7+8UESRAbiekPQDAAAAVxw/tEPh1W+Sj69flmXJiXHaF/W74i6e1m3dBkmSNv3+tfwCQ5SanCiLxUfhN9bXDTUaavn3E/Jsyyf9orvDB4AsSPoBAAAASX/8PEW75j8ta0hdDf7vvizLZwwLk9lIlSTt3zhHSWe2ySf9UpZyARHdlXx4YaHHCwDOIOkHAACAV0pNTtTXY++XYdh037AvVLpMRa1eOFlpKYkKq1JHTW6916H8zmUfySTJEr9fkwda5FelnWo07aE7egzRn+t/sif8kpR24vcc/5Em4QdQWFx9XJ9E0g8AAAAv9b/RPZR24ndJ0qw3blPDTiO054ehkqSDkuo0vqSgkDLZrmuSTWknl2v/yeW6cHKfLmybUkRRA4B7kfQDAACgREtOjNOWFd+qefuHFBBUyj4/M+GXJEviX/aEP9OXz5WTSYYkyRpUU5bEv7Ktn4QfQElG0g8AAIAS7X9vd5X1zBrtXj1dg8asy3uFKzITfkk5JvwAUFTSJI26atpdSPoBAABQIhk2m0xms6xn1kiSbGfXy7DZPBwVAORPmqQ3C6Fekn4AAACUOMvmva99S96QxZboMH/qIIuHIgKA4sns6QCKg+joaHXo0EH169dXo0aNlJCQ4OmQAAAArjsLpv5L86c8J0navXmpZr7ZVedPH9bFs8c0861uilqzQF++fa/WLv5cBxe9mCXhB4DiwMi7SLZMkupfebl+j/6c0dMvqV+/fnr77bd1xx136OLFi/L39/d0SAAAACXe+dOHFVyqnAKDS+dZ9uDONTqzYYIk6VyP57R6UmdJ0uyRkfItW1fG+U3aMH2RJOnP6B8LLWYA8JRASbuuTAdLctfXmtd90r9r1y75+vrqjjvukCSVK1fOwxEBAACUfOdO/qXv/11LNpO/npmWnGvZhTNf0/HVo+3vv/93Lfu0j/WyjPObCi1OAPB2xX54/6pVq9SjRw9VqVJFJpNJCxYsyFJm4sSJioiIUEBAgFq3bq2NGzc6Xf+BAwcUEhKiHj16qFmzZho9enTeKwEAACBHG3/7n+aO6ShJMhspOnvioCY+VV5fjblPv3w5UhOfqaRdmxYrLTVZk57wc0j4AQDuVex7+hMSEtSkSRMNGDBA9913X5bls2fP1ogRIzR58mS1bt1aEyZMUOfOnbVv3z5VrFhRkhQZGan09PQs6y5dulTp6elavXq1oqKiVLFiRXXp0kUtW7ZUp06dCn3bAAAAvNG2bx7T1bfTmz+yjnwkJR6cr8SD8+Uj6Y/JXbWv+WCZDXc+mAoAcK1in/R37dpVXbt2zXH5+PHjNWjQIPXv31+SNHnyZC1cuFDTp0/XK6+8IkmKiorKcf0bbrhBLVq0ULVq1SRJ3bp1U1RUVI5Jf0pKilJSUuzvY2NjXd0kAACAIvf9pGd1ZstnMpdtqEdeW6RSZSrkWn7pt6N1eOt8/XPkrwoKKSNJ2rjsa22a86x80i/KGlJXFr9gpSeelyX5hPxuaC+bNVXW0384HdO5LZ8WZJMA4LpkGK7dKrDYD+/PTWpqqrZs2aKOHTva55nNZnXs2FHr1q1zqo6WLVvq7NmzunTpkmw2m1atWqWbb745x/JjxoxRaGio/ZX5ZQEAAEBxdm7zJzIbKdLFLfph8jN5lo/+9TUZFzbrx6nD7PO2ff2ofNIvSpIs8fuli9vkk3xMJtmUduJ3lxJ+AEDRKPY9/bk5f/68rFarKlWq5DC/UqVK2rt3r1N1+Pj4aPTo0Wrbtq0Mw9Bdd92lu+++O8fyr776qkaMGGF/HxsbS+IPAACKnXkfP6W0lHjFnNgmc+weh2UJB77TxGfCVSairdJTEpR85Jcc67m86ytNGfhVYYcLACgkJTrpd5e8LiG4mr+/P4/0AwAAxVpaarIuRE2VlPOwTp/UM4rfP7foggIA5CpN0rirpt2lRCf9YWFhslgsOnPmjMP8M2fOKDw83ENRAQAAuG7/9pWyWtO04n//UsM7B8ti8VPjW3sqJLS8Ni//VqXKVtKejT/qxLZvJZNJ/mVqKeXSQTXrOVo75gyQEdpQpss7ZStVT+a4fZ7eHACAi9IkvVQI9ZbopN/Pz0/NmzfXsmXL1LNnT0mSzWbTsmXLNHToUM8GBwAA4ATDZlNyUryWf9ReUkbP/O75gyVJUYvGqPk9byjqm8ft5TP/ebOeOSUfSTvmDJAkmS7vzFifhB8AcJVin/THx8fr4MGD9vfR0dGKiopSuXLlVL16dY0YMUJ9+/ZVixYt1KpVK02YMEEJCQn2u/kDAAAUV+dO/qW5bzSWT/mG2S63JBzSwS0/FXFUAABPMEmqfmX6qCTX7tGfs2Kf9G/evFkdOnSwv8+8iV7fvn01c+ZM9enTR+fOndPIkSN1+vRpRUZGavHixVlu7gcAAFCYLl88re8mPKq0E7/LXLGN0uJPSTLrrqe+1NZl0xXZ/nHt3rBAFh8/nVzzntL9Kson9awskoxzG3Ksl+vuAaBkcfGJenaBkg5fmQ6WlOiecGQyXH3IHxzExsYqNDRUly9fVunSpT0dDgAA8JCpr7SRcW69p8MAAHjYx0ulXcdzXn51Cm4ymezTQZISrkznlPTPmzdPW7Zs0ZgxYyTJqTy02Pf0AwAAFIboPRv0y3+768bWg1S2Uk1FLXhBPtZYSZLVUkoyrAqtc7dadxumJR/foxq3DVFS3Hmd3zrJXochk57+3KZJw2+WOc65xwUDAFCUSPoBAECJEnvprEqFhslkNiv20lmVLlsx1/I2q1UJcRdVqkwFSVJKUoIMw6bFUx6XT9oFnfjjXZ2Q4z9FFmucJCl+3xwtPfCzfGyJOrbyP1nqNsnQttXfkfADAIotkn4AAFBi7N++Uss/ai+jTCPVueUxHVz8kkLq9tajL8/JcZ3J/6onS8IhdX1lq6rWbKzpz5aTyUiXzadMjs+wv5rFlvtVlRtnPuDiVgAAUHSc+VsHAABQpFKSEmTYbPb3qcmJslmtWjP/HUmSKeZPHVj8iqSMG92lJCUoJSlBifExOnfyLxk2m+JiziklKUGWhEOSpOXfvK6/9qyX2UiVSTb5pF8s+g0DAKAArr4HgLPo6QcAAMXKieid+vntRjKVb6En39ukxPgYfTG8kgz/ijLSYu3/vJj095cCM4eG5Flv8pFftOzDXwopagAAiieSfgAAUKysmPOWJMm4sFmSFLX6O5mNVCk5l1shAwBQwqVLmnjVtLuQ9AMAgEKx9NvROr7nd/X99yJZfHwlSVtXzdOmL3qrcpsXVK5ybe1e9bmM85skSTf3nKg7uj/t8Fz6KQNdH8YIAEBJlCppaCHUS9IPAADsDu1aq8sXTqpZW9dvTmdNT9PaxdN0c4uuCguPUPSvr0mSlnz9H3V7PKP3ftMXvSVJp9a9r1PXrL9nwRDFnIkuUPwAAMARST8AALD7bfxtkqSwyltVvU5Tl9b9cdrzOrvpY+34sbSGTL1sn58Qc216n7PY8yT9AIDrV9iVn+fdWCdJPwAAyOLo/o1KSY7X0on3qU674brr4dc0f/Iwndw+R71f/0MHd6zUn/OeyHZdH2usw7D8i9unacrAaU61m3DgO7fEDwBASRMk6dyV6WBJuT8w1nkk/QAAeJlzJ//SpXPHVLdJuyzL9m9fqeTEWB3bt15dH3tLMRdO6o8fP9Jdj4xUclKcvdz2n16XT9p5+UiK/u11zTr2p+L3zZaPpLlv3yaf1LNFt0EAACDfSPoBAPAy3/+7liTJ98UNqnFTK/v8I/u3aPlH7e3vvzy6TSlHFkmSZqz/r2SyyHxlmU+a48DC+H2z7dMk/AAAlBzmvIsAAIDi5OSR3frhs+eVEHtRkmTYbJo9ob9mjLpLsZf+Tsh/+fg+pSQl6ET0Tv3w2fNaPLaFQz2ZCb8kmY00mW3JRbMBAACgyNDTDwBACbPg7day2OI1+/h2DXjzN/06513F/DlTkvTtmO72cj7JJ/TdxEGK2btAFiPJQ9ECAABPIukHAKAIGTabfps7VnUiOymiXgtdvnhaaxdO0m13D1HpshXt5c6d/Et//PRfnftrnawpl9Xs7te19efRMsftleVKmbTjy7I8x964sNnhfdyeb+zlAQDA9YekHwCAIrT023d0eNlI/bX0//TU54a++c8/ZIrdrSPbvtfT4/+0l5vzZkv5pGcM3zdLivrmca7JAwAALuP/BwAAcpGeliprepoMm00pSQnZlklLTZbNapUk2axWXTp/wr4sNfnvB+5Y09N0fPcy+/uE2Isyxe6WJJku71RqcqJiL51VWmqyPeEHAADXh3RJM6+80t1YLz39AADkID0tVVMGh0kWP/mWqSPj3Hp1f22HqtZsZC+TlBCrmc9VlM0/TEMmHtdnT2b8afWt+g+1vvsF/TG5qwJr9NA/X52vKUMqypIeY1/3f/8q79DejCHBRbJdAACg+EmV1L8Q6qWnHwAASaeO7NHUl1pq8/Jv7fOOHYqSjy1OPmkXZJxbL0laPvsNh/W2r50vs5Ein+QTDvPTji/T6q+flyQlRf+kS+dPOCT8AAAARYGefgDAdeP4XxnXzN8Q0UDrf/1CoeVvkH9giMqEVdWCCffLHLtHW/73sE4f3q6Off5PezctzFJH4sH5+unzl3Ri91JVb9JDhzf9z36jvInPhDv8Yc0cui9Jc8d2k+Mt9wAAABwFXfmZmGsp15D0AwCuCylJCVr4TmNJUpXbX9bJP8Y6LLf5lLMPfzvxx7v64o93c6zr5NpxMkk6tnK7w53xfVLP5LiO6fKufEYOAACuB0GSMu8eFCz3Jf4M7wcAlChH9m/R9DfuUvTejbJZrfpqzH1a/v2HkqRDu9bq00HBmvpKG1nT0zRpeD1NfDJU508fVuylvxPyaxN+Sdw4DwAAeCWSfgBAifLzB52UdvxXLfqws37/7gMlHpyv/QtHSJKWTOgkiy1Rxrn1+vz1O2SO2y8fa6y+++A+mcz8yQMAAIXPMDwdgSP+AwIAuN1P01/Wp8/Wcnh0XXZOHdmjT5+toSVfv61zJ//Sp8/W1C9fjnQoc/bEQU0ZaLK/fNIvSZIs6TE6dWi9vdyUgSZZbH8PhDPObfi7kovb9N1rNdywZQAAACULST8AwO1OrnlPlsS/9OOnT+Za7qdP+8qSeFiHl/1bP346QJbEaB1b+R+HMt+9f0+O6ycenO+WeAEAALwVN/IDALgs6o/5WjfvFRnWNJWq3ESP/V/2yXfS0SX26Tn/HahLO6Yr4h9v6bbug/Xl89VkMZLsy9NPrbRPTxxSXT7JxyTx7TQAAEBBkPQDAFy2YcZ99mQ88VC0UpMT5RcQlKWcybDapy/tmC5JOrxspC6c2O2Q8F8rM+EHAADA30wm1x8ATNIPANeRM8cPKGrVt+pw3/M6vG+TThzapg73Dc+27PSR/1DS+T168PU/9PvstxRS9gb5B4Xq4Povszxv/uux9yvl6GLd9tQvWjOlm8Oy9b9+oahvBzisE7f3W7duFwAAQElnlTT3qml3IekHAC+TGB+joJAy2S777s2mstgSNO/EbnviXbpcFTVv/6BDuag1C5R24nf5SPr+37UkSTFXlmX3/XLK0cWSlCXhl6Tt3/bLdh0AAAD8LUXSg3mWch2XSgKAF/n6/Uf01XNltfrnSdkut9gSJEmXov++fv7Y/rVZyh3bvz7LPAAAAJQ89PQDQAmybskMHdzyox5+cbZ8fP2yLI/b840kaff8wSpdroqilk1Tr6Gfq3TZig7lfFJO2afPbPivpmz4b+EGDgAAAI+gpx8A3MRmzfvqq2vLGDZblnk5vbemp2nHnAFKPLRAP894yaGczWqVYbM5rLf+855KPvyz5k14RDar1an4AAAA4BlBkowrr6y3R84/evoBwA1iLpzUrFfqKbByKw14a1m2ZWa+1U2Jx1aq95t/qkKVmpKkySPqy5YSo6c/PiaLj69+m/OeDiz9txr1/Ei33/2Uvv90iM5snSaVrivz5Z32us5s+K8+2/BftRu6TDVuaq1pw6vJp3REtu2mHV+mz57k1z0AAEBJZxiGy+vwXyAA5ENqcqKO7N+skNAKSklO0MbFk+Rji1faid8lSXu2/iZfv0CVKlNRlarW0aXzJ5RyZJEskn6Z9qw69R2nKjfWlzlun8ySfvnydYVHNNGhJS/LLGnX/KdVLryGzm35NGNI1lUJ/9VWfvIPbanXRz7pl6SLl4po6wEAAFBSkPQDQD58NqK2w3XxVzvw52qtmtjJ/v7+t//Sd6/XtL9PPvKLfnrrF3UcscY+7+Sa93RyjUM1Wj2ps1OxxO+b7ULkAAAAuJ5wTT8Ar3D+VLSs6WlOlb188bSSE+N09sRBGTab0tNSdf704Rzr3LP1N505fkCSdO7kX9q77fccE35JWjr5YYf3+7b9lm25Fd++5lS8AAAAQH7R0w+g2LNZrTJbLDku37HuJ62bdo+swbU1+KMDudYVe+msvn2xsv19SL0HFXNknXySj+nO51aqTuO2kqSdGxdleeZ8rc5jdWjJy3nG65N8wuH9zu+ezLZc+skVedYFAAAAFAQ9/QCKtc9fb6fJT4cq5sLJHMtsWviBJMmScDDP+vZuWerwPn7fHPkkH5MkbfjlY/v8jT+/n2VdZxJ+AAAAoDihpx9AsZZ+apUskn6fM1r3PfOJJOmnz1/Syd1LJIuvAktXUfqplfbye7b8qtVzXlO5qk31wLNTtPz7D7V76TvySbuQZ1sJB+ZpykBTYW0KAAAAkCOrpIVXTbsLST+AEuHUjnk6fuhJWa1pOrl2nH1+0oUtDuVWfXqXJOnC+U2Spmj/whH8ogMAAECxlyLp7kKol/+FAXiMYbNJkkzmrFcaGTabw3yf1DP6aUwLmQ3nbtZ3df0AAADA9YqkH4BHGDabJj1XR5L0zH8POCT4m5d/q01f91fNDv/nsI4rCb8kTR2U883/AAAAgOsBST8Atzt6YJuOH9yqW7sOzHZ5anKiln7zH1kS/5IknT99WHu3LtGRnb/Lmpak5MMLZZZ0eNnIIowaAAAA8JwgSWevTFeUlOimekn6AbjdonebSZJ8fP3VquM/syyfPf5RJR5a4DBv9/zBRREaAAAAUGwFF0KdJP0AcrT+1y90fO9a3T9kkkxms05E79TPbzeSzRygjsOWaunHPRRc9Vb5BYbKZDIrMea4Uk5vUuag+m3fPKZt3zwmc8U2sp1dp5vu+UhlK0ZkSfjnju0qBuIDAAAA7kfSDyBH27/tJ0la9VNDtbv3Wf34TguZJZltyfp9Qlv5SEo5skgpV62TXfJuO7tOkrT3x2HZtmOJ3+/OsAEAAACPMTwdwDVI+oHr1Kkje/THj+P1jz4jdfDP1dqzZpas6ckqVaGmbqhzi6K+e86ewJ87tksLpv5LZiMl1zoBAAAAFC8k/UAhi967UaeP7FKbzv09HYqD78fcIZ+0C5odvV7myzvt8y+e+F0Xo6Y59Nif3bdElsTDRR4jAAAAgIIh6QcK2dJxrSVJIaEV1OiWu3Msd/5UtMpVrK4LZ44oLDzC4RF2F84cVWBwaaUkJ6hs2A06c/yA4mLOKqJeS1ksPjp/+rAqVKmppIRYpaUmK6R0eV08e1RhlWs41P/XrjWqWqeZ4mPOySftgiQ5JPw5IeEHAAAAPM9kMrm8Dkk/UEQO71qVY9K/efm32vK/h+3vfSq308C3V2Sst2+zlrzX0r4srNnTOr91sv29ueItsp1dr5t7TtTuH4bJZFhlDaopS+Jfat3/e0Xe3ktbV83Tpi96S5K2FcK2AQAAACgYm6QVV027iznvIgDcwTByvqXH5oVjHd6nn1ppn96waKLDsqsTfkmynV0vSfpzyXsyGVZJkiXxL0nSpoXvXan/vXxGDQAAAKAoJEvqcOWV7MZ66ekHClFyYlyWeXEx57Rt9Vzd0qmfLD6++n7SYJkuRWUp99mrbZR6+Zh8Uk441ZZP0pEs82xn12vKQNeHAAEAAADwDiT9QCGaNeaeq95l9PR/NbKNLAmHdHjHEpUKi9DFqGnZrms7u54TFAAAAECBkFPA6yUnxunLt+5SRJO7ddfDr+WrjpU/fKx9a77Ugy//oNCy4fr89baynlkjSUr3LS+fUtVVOry+YnfPkkwmpftXkU9y9j30loRDkqSkv35U0l/52yYAAAAA3iVI0uEr0xGSEnMol9tlw9nhmn54vYUzXpJxbr2if3s933Xs/XGYjAubtWDiE9qz9Td7wi8p4y74F7dlJPySZBg5JvwAAAAAkJMKV17uRE8/vMqSr/+jw8tGSpLSzSG6vf//lBh7Lku5VT9O1M6Fr6th97f15y9vStYk+dji7cvr9/pUu+cPliTd//bf3fEpRxZp1aTFys9V8qfWfaAp6z7Ix5oAAAAAkD/09Ev68MMP1aBBA9WvX1/Dhg1zebgEio/MhF+SfGzxWjPjkWyfZbnnh6GypMdozw9D5ZN2ziHhl2RP+CVpzrtdHJaZxPEBAAAAoGS47nv6z507p08++US7du2Sr6+v2rZtq/Xr16tNmzaeDu269+Xb9yop+keHZ9avXfy5/pz7hCSpym0v6eimL9S23+c6uG2JTu74PssBbbElKvHSUfv7/NzJ3hK/P7+bAAAAAOA6U9z6kK/7pF+S0tPTlZyc8STEtLQ0VaxY0cMRQZKSon+U5PjM+syEX5JOrnlPPpJWTXtIPrb4HA9m4/zGQowSAAAAAIqvYj+8f9WqVerRo4eqVKkik8mkBQsWZCkzceJERUREKCAgQK1bt9bGjc4neRUqVNALL7yg6tWrq0qVKurYsaNq1arlxi3wHsu//1Ar5v83y3zDZtMPnz2vqDULcl0/Ifai5n3ytKL3bND8ycO0a9NiSdKJ6J2a9/FT2rvtd33+ejtNesLPvizTlIEmffpsjWzrvXZoPgAAAAAgQ7Hv6U9ISFCTJk00YMAA3XfffVmWz549WyNGjNDkyZPVunVrTZgwQZ07d9a+ffvsPfaRkZFKT0/Psu7SpUsVGBion3/+WYcPH1ZgYKC6du2qVatWqW3btoW+bSXJ5YuntX/hCElSq059FRRSxr5sxYKPdHr9eJ1eP16Rt+U8lmXO+IeUeuxXLd02RZJ0dtPHatDS0IIxt8nHGquVUVMlZXwT9cfkrlnWtyQedtv2AAAAAEBxYpO06appdyn2SX/Xrl3VtWvWBDDT+PHjNWjQIPXv31+SNHnyZC1cuFDTp0/XK6+8IkmKiorKcf25c+eqdu3aKleunCSpe/fuWr9+fY5Jf0pKilJSUuzvY2NjXd2kEiku5u874KemJDok/WePRDlVR+LpbdkecD7W62MfAgAAAEBOkiW1KoR6i33Sn5vU1FRt2bJFr776qn2e2WxWx44dtW7dOqfqqFatmtauXavk5GT5+vpqxYoVevLJJ3MsP2bMGL355psFjr2kMWxW+/TssfcoIvJedX7k31r67WjF7PzCviy7G+XZzAEy25KzPdjyc2M9AAAAAIBziv01/bk5f/68rFarKlWq5DC/UqVKOn36tFN13HLLLerWrZuaNm2qxo0bq1atWrrnnntyLP/qq6/q8uXL9texY8cKtA0lQWpyomIunPh7xoUtOrxspJISYhX962t5rm+2JRdidAAAAACAnJTonn53eeedd/TOO+84Vdbf31/+/v6FHFHxYbNaNWNIcLbLvhwWWsTRAAAAAIB3CpS0+8p0fUlJbqq3RCf9YWFhslgsOnPmjMP8M2fOKDw83ENReZcd63/ydAgAAAAA4PVMkiKumnaXEj2838/PT82bN9eyZcvs82w2m5YtW6Y2bdp4MDLvEH/5gjZM7+XpMAAAAAAA+VTse/rj4+N18OBB+/vo6GhFRUWpXLlyql69ukaMGKG+ffuqRYsWatWqlSZMmKCEhAT73fyRf2dPHPB0CAAAAACAAij2Pf2bN29W06ZN1bRpU0nSiBEj1LRpU40cOVKS1KdPH73//vsaOXKkIiMjFRUVpcWLF2e5uR9ct3HJJE+HAA966nOjSNtreP9Uh/eBNXO+oWZB+VbpIGtgdYd56QHV7NNPfW5k2X5b6ZsK3K7VHKSQeg+6tE66X8UCtwsAAIDrV7Hv6W/fvr0MI/fkY+jQoRo6dGgRRXT9uLBnQfE/QOC1TKZi/50kAAAAUOzxXzVy5GON9XQIuJ6Z3Hn7EgAAAOD6REcugGLBMGyeDgEAAADwGEPSrqums2PKR8cYST+AYoqBSAAAALh+JElqWAj18l81gOLhmq8zi93ofnfd1zCPe5QAAAAA7kTSD6CYKm5ZPwAAAFDykPQDKJbyc70SAAAAUFIFStp55RXoxnq5ph9AsWDomhv5kfRfwX4AAAC4HpgkNbhq2l3o6QeQo3S/ioVSr7limyzzbqx3i8P7Wk27FUrbAAAAwPWEpB/IQ1izp7PMC234uKq1+3e+6uv+2o48y6SbS8mvWqccl1e57aUclwXV7qXQBo/JUul2Wc0BV9UZIkmq0GKoStd/zKlYH317m0IbPq7gOg+oTrf3VbnNC+r6ylbV6/FfVWo9XD6V26p2l/fU9OGvdOuTP6vRA9PUe8xRBdW+/+92A29UxZbPyv/Grmrx2GyVadRPj4/81aGd1v2/V/U6TdXrrQNSueaq2328bunUV3e9uMGpOIsEHe4AAAAogRjeD+TCGlxL9w+ZpCkDJzvMf+hfX0iSPl09ThZbssMyQ2aZrh2qfpWqNRvl2a5/hSbqP2qppgzMPtPsMWCsJm78XD5pF7Ise+zV7x3eZ9ZR/uZeemjEl/b5k56YLbORmmscZcpXsW/r1arXaZrreo+9Os/erk9gefV6+iP7subtH3Qoaw2pq8jbe0mSKt5QW0+N22xfVuOmVnnuzyLDTfcBAABQAtHTD+QqP927dAnDnTieAAAAkH8k/YCbGUWVpHnJ8965Xx8AAAC8SXH7N53h/YC7mUwMBQcAAADgEkPS4aum3YWefuQouM4D9umnPr8+s9j8PSuermsAAAAArkmSVOPKK8mN9ZL0IxfXZ6LviuzT+2Ka9DOOvlgwitt4LwAAAHg1kn7A3Uiur+Et+4NkHQAAACUPST+Qi/zclI/UEAAAAICrAiRtvPIKcGO93MgPyEVenfaGkV0Bb+nZBgAAAFBUzJJaXjXtznoBl1nNwS6vExDR3bm6g2tnO99myvodVZM+M7Itmx5QTVafMo4zyzV1bMfkn2v9VnOgOj0xXZJUte3/ZdtO63/O/PtN2UhZzQG6te8sWS2lsi1vDYqQJAXV6imrOSTbMtcfviQBAAAACgs9/chRTvcbM4W11OCxG2XYbJoxqpPSTvwuSXryM2uu9ZnMZhk2myTp9LF9+vGt+pKkdkOXqV6T9jIMQyaTSSazWVMGOiaC6b7lNfjTs9nW2brj41nWGfzx4Szznhq3VWsXf64/5z4hSer/37P6clioJKlGp3fU6cFXNHWQJaO9gBs0+OOjMpkzvhfr3vcd/ZSeppNrxzm036ztA9r8VcZ0vdv7q909Q2Uym9Xk1ntz3AeS9Nj/zZdhs9nbKwomkmsAAADgukPSj3zISB4zE1j7XHPeA0cyy1xd1my2yGQ255GSmnKsP7v5OZY1ma+aNjlM52d7cmrXlX0BAAAAAIWFrAPIRsnoFeeWgUXLHfs7P8cVnzMAAADyj6Qf162re/3dU19J+KIAAAAAwPWE4f0AAAAAABQD5wqhTpJ+5MLm6QDczjDyv01GTnc2BFzCcQQAAICsEiVVzKNMfkYXM7wfrmMYe8lUbD+34hoXAAAAUPKR9AMlFj3G1we+FAEAAED+kfTD47gBHgAAAIDrXYCk5VdeAW6sl6QfLgsJq2mfLl2htgcjKWr0rAMAAAAoHGZJ7a+83Jmok/TDac3/+Y0Ca9yjXs9Mts+7Z9CHCqzVU5EPf+nByNzPYEg1AAAAAC/A3fuRs2vuVt+iw0Nq0eEhh3l+AUF6/P/mF2VUbuPuywpMpuL9HVpxjw8AAACA+5EF4PriRSP0S8xYhBITKAAAAOB9SPoBeBZfCgAAAACFhqQfnldC7t5vGF40TAAu4+MHAABAScQ1/QAAAAAAFAMJhVAnST9yRMcm4H6MGAAAAPBu+f13L1FSiDsDuYLh/UAJxeUGAAAAAPJC0g84yTBsng4BAAAAAFxC0o/rytWJu8ns5sO/hNyQEAAAAEDx4y/p5ysvfzfWyzX9ADysZHxZYuIuFwAAAChEFkndr5p2F3r6kbMiGs5uMnEYAgAAAEBhINsCnFaye3r5cgUAAAC4/pAFAAAAAADgpUj6AQAAAADwUiT9AAAAAAB4Ke7eX0CGkXGdd2xsrIcjcb+k5DQlpWZMu3v74uLi7XXHxSdkqT9zWaZ0w5ZnDFevk1n22nkJiUkO25Q5nZCY7PA+3Zy1vcSklCz7w7DZrqojyeX9dO12ZrKkpDvEc62MZYZ8rNkvy66NxORUh2VJqYbMudymoKCfuT32FGu2dWUut+Ww/O9yhtvv75+ekq7kVJssV+3fdLNNPtd8vlfvf1uKVeYcPg9nWc2GfK46r5yKVX/HBQAAgOLPmsf90HP639eQFHvVdHYSEhKUkpLy9zpG3vcdMxnOlEKOjh8/rmrVqnk6DAAAAADAdebYsWOqWrVqrmVI+gvIZrPp5MmTKlWqlEymkvG8cVe0bNlSmzZt8nQYJY637reStF3FKVZPxFJUbRZWO+6sNzY2VtWqVdOxY8dUunRpt9SJkq04/X4oSbx1v5Wk7SpOsfK3zbP18rcNhmEoLi5OVapUkdmc+1X7DO8vILPZnOc3KyWZxWLhF0k+eOt+K0nbVZxi9UQsRdVmYbVTGPWWLl262BwT8Kzi9PuhJPHW/VaStqs4xcrftuJRL3/brm+hoaFOleNGfsjVkCFDPB1CieSt+60kbVdxitUTsRRVm4XVTnH6/OB9OL7yx1v3W0naruIUK3/bik+9QF4Y3g8A8GqxsbEKDQ3V5cuX6Q0BAHgF/rbBFfT0AwC8mr+/v9544w35+/t7OhQAANyCv21wBT39AAAAAAB4KXr6AQAAAADwUiT9AAAAAAB4KZJ+AAAAAAC8FEk/AAAAAABeiqQfAAAAAAAvRdIPALhuHTt2TO3bt1f9+vXVuHFjzZ0719MhAQBQIDExMWrRooUiIyPVsGFDffbZZ54OCR7GI/sAANetU6dO6cyZM4qMjNTp06fVvHlz7d+/X8HBwZ4ODQCAfLFarUpJSVFQUJASEhLUsGFDbd68WeXLl/d0aPAQH08HAACAp1SuXFmVK1eWJIWHhyssLEwXL14k6QcAlFgWi0VBQUGSpJSUFBmGIfp5r28M7wcAlFirVq1Sjx49VKVKFZlMJi1YsCBLmYkTJyoiIkIBAQFq3bq1Nm7cmG1dW7ZskdVqVbVq1Qo5agAAcuaOv20xMTFq0qSJqlatqhdffFFhYWFFFD2KI5J+AECJlZCQoCZNmmjixInZLp89e7ZGjBihN954Q1u3blWTJk3UuXNnnT171qHcxYsX9fjjj2vq1KlFETYAADlyx9+2MmXKaPv27YqOjtbXX3+tM2fOFFX4KIa4ph8A4BVMJpPmz5+vnj172ue1bt1aLVu21CeffCJJstlsqlatmp599lm98sorkjKGPnbq1EmDBg3SY4895onQAQDIVn7/tl1t8ODBuvPOO/XAAw8UVdgoZujpBwB4pdTUVG3ZskUdO3a0zzObzerYsaPWrVsnSTIMQ/369dOdd95Jwg8AKPac+dt25swZxcXFSZIuX76sVatWqV69eh6JF8UDST8AwCudP39eVqtVlSpVcphfqVIlnT59WpK0Zs0azZ49WwsWLFBkZKQiIyP1559/eiJcAADy5MzftiNHjuiOO+5QkyZNdMcdd+jZZ59Vo0aNPBEuignu3g8AuG7dfvvtstlsng4DAAC3adWqlaKiojwdBooRevoBAF4pLCxMFosly82Lzpw5o/DwcA9FBQBA/vG3DflB0g8A8Ep+fn5q3ry5li1bZp9ns9m0bNkytWnTxoORAQCQP/xtQ34wvB8AUGLFx8fr4MGD9vfR0dGKiopSuXLlVL16dY0YMUJ9+/ZVixYt1KpVK02YMEEJCQnq37+/B6MGACBn/G2Du/HIPgBAibVixQp16NAhy/y+fftq5syZkqRPPvlE48aN0+nTpxUZGamPPvpIrVu3LuJIAQBwDn/b4G4k/QAAAAAAeCmu6QcAAAAAwEuR9AMAAAAA4KVI+gEAAAAA8FIk/QAAAAAAeCmSfgAAAAAAvBRJPwAAAAAAXoqkHwAAAAAAL0XSDwAAAACAlyLpBwAAXi81NVW1a9fW2rVr3Vrv4sWLFRkZKZvN5tZ6AQBwF5J+AABKmH79+slkMmV5HTx40NOhFVuTJ09WjRo1dOutt9rnmUwmLViwIEvZfv36qWfPnk7V26VLF/n6+mrWrFluihQAAPci6QcAoATq0qWLTp065fCqUaNGlnKpqakeiK54MQxDn3zyiQYOHFgo9ffr108fffRRodQNAEBBkfQDAFAC+fv7Kzw83OFlsVjUvn17DR06VMOHD1dYWJg6d+4sSdq5c6e6du2qkJAQVapUSY899pjOnz9vry8hIUGPP/64QkJCVLlyZX3wwQdq3769hg8fbi+TXc94mTJlNHPmTPv7Y8eO6cEHH1SZMmVUrlw53XvvvTp8+LB9eWYv+vvvv6/KlSurfPnyGjJkiNLS0uxlUlJS9PLLL6tatWry9/dX7dq19fnnn8swDNWuXVvvv/++QwxRUVG5jnTYsmWLDh06pO7du7u4l6XDhw9nO6qiffv29jI9evTQ5s2bdejQIZfrBwCgsJH0AwDgZb744gv5+flpzZo1mjx5smJiYnTnnXeqadOm2rx5sxYvXqwzZ87owQcftK/z4osvauXKlfrhhx+0dOlSrVixQlu3bnWp3bS0NHXu3FmlSpXS6tWrtWbNGoWEhKhLly4OIw6WL1+uQ4cOafny5friiy80c+ZMhy8OHn/8cX3zzTf66KOPtGfPHk2ZMkUhISEymUwaMGCAZsyY4dDujBkz1LZtW9WuXTvbuFavXq26deuqVKlSLm2PJFWrVs1hNMW2bdtUvnx5tW3b1l6mevXqqlSpklavXu1y/QAAFDYfTwcAAABc9/PPPyskJMT+vmvXrpo7d64kqU6dOnrvvffsy95++201bdpUo0ePts+bPn26qlWrpv3796tKlSr6/PPP9b///U//+Mc/JGV8cVC1alWXYpo9e7ZsNpumTZsmk8kkKSMhL1OmjFasWKG77rpLklS2bFl98sknslgsuummm9S9e3ctW7ZMgwYN0v79+zVnzhz9+uuv6tixoySpZs2a9jb69eunkSNHauPGjWrVqpXS0tL09ddfZ+n9v9qRI0dUpUqVbJc9/PDDslgsDvNSUlLsowIsFovCw8MlScnJyerZs6fatGmjUaNGOaxTpUoVHTlyxIW9BQBA0SDpBwCgBOrQoYMmTZpkfx8cHGyfbt68uUPZ7du3a/ny5Q5fEmQ6dOiQkpKSlJqaqtatW9vnlytXTvXq1XMppu3bt+vgwYNZetSTk5Mdhr43aNDAIdGuXLmy/vzzT0kZQ/UtFovatWuXbRtVqlRR9+7dNX36dLVq1Uo//fSTUlJS1Lt37xzjSkpKUkBAQLbLPvzwQ/uXC5lefvllWa3WLGUHDBiguLg4/frrrzKbHQdLBgYGKjExMccYAADwFJJ+AABKoODg4ByHs1/9BYAkxcfHq0ePHho7dmyWspUrV3b6rv8mk0mGYTjMu/pa/Pj4eDVv3jzbO9lXqFDBPu3r65ul3sxH3gUGBuYZxxNPPKHHHntMH374oWbMmKE+ffooKCgox/JhYWH2LxWuFR4enmU/lipVSjExMQ7z3n77bS1ZskQbN27M9jKBixcvOmwjAADFBUk/AABerlmzZvruu+8UEREhH5+sf/pr1aolX19fbdiwQdWrV5ckXbp0Sfv373foca9QoYJOnTplf3/gwAGH3u1mzZpp9uzZqlixokqXLp2vWBs1aiSbzaaVK1dm6YHP1K1bNwUHB2vSpElavHixVq1alWudTZs21aRJk2QYhv2yA1d89913euutt7Ro0SLVqlUry/LMkQxNmzZ1uW4AAAobN/IDAMDLDRkyRBcvXtTDDz+sTZs26dChQ1qyZIn69+8vq9WqkJAQDRw4UC+++KJ+//137dy5U/369csyhP3OO+/UJ598om3btmnz5s16+umnHXrtH330UYWFhenee+/V6tWrFR0drRUrVmjYsGE6fvy4U7FGRESob9++GjBggBYsWGCvY86cOfYyFotF/fr106uvvqo6deqoTZs2udbZoUMHxcfHa9euXS7stQw7d+7U448/rpdfflkNGjTQ6dOndfr0aV28eNFeZv369fL3988zDgAAPIGkHwAAL1elShWtWbNGVqtVd911lxo1aqThw4erTJky9sR+3LhxuuOOO9SjRw917NhRt99+e5Z7A3zwwQeqVq2a7rjjDj3yyCN64YUXHIbVBwUFadWqVapevbruu+8+3XzzzRo4cKCSk5Nd6vmfNGmSHnjgAQ0ePFg33XSTBg0apISEBIcyAwcOVGpqqvr3759nfeXLl1evXr2yvewgL5s3b1ZiYqLefvttVa5c2f6677777GW++eYbPfroo7leYgAAgKeYjGsvzgMAAJDUvn17RUZGasKECZ4OJYvVq1frH//4h44dO6ZKlSrlWX7Hjh3q1KmTDh06lO0NDfPr/PnzqlevnjZv3qwaNWq4rV4AANyFnn4AAFBipKSk6Pjx4xo1apR69+7tVMIvSY0bN9bYsWMVHR3t1ngOHz6sTz/9lIQfAFBscSM/AABQYnzzzTcaOHCgIiMj9eWXX7q0br9+/dweT4sWLdSiRQu31wsAgLswvB8AAAAAAC/F8H4AAAAAALwUST8AAAAAAF6KpB8AAAAAAC9F0g8AAAAAgJci6QcAAAAAwEuR9AMAAAAA4KVI+gEAAAAA8FIk/QAAAAAAeCmSfgAAAAAAvNT/Axyy+62+kHSmAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -438,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 13, "id": "6a2ca110", "metadata": {}, "outputs": [ @@ -459,19 +459,19 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 14, "id": "bd08c356", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Array([ 5.5163288e-24-2.8030924e-24j, 9.7971486e-25+6.1067470e-24j,\n", - " -6.1187298e-24-8.8225277e-25j, ..., -0.0000000e+00+0.0000000e+00j,\n", - " -0.0000000e+00+0.0000000e+00j, -0.0000000e+00+0.0000000e+00j], dtype=complex64)" + "Array([ 5.5581057e-24-2.7243789e-24j, 8.2296243e-25+6.1320864e-24j,\n", + " -6.0873832e-24-1.0901924e-24j, ..., 0.0000000e+00+0.0000000e+00j,\n", + " 0.0000000e+00+0.0000000e+00j, 0.0000000e+00+0.0000000e+00j], dtype=complex64)" ] }, - "execution_count": 59, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -486,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 15, "id": "5616c838", "metadata": {}, "outputs": [], @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 16, "id": "05c1e340", "metadata": {}, "outputs": [], @@ -558,13 +558,13 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 17, "id": "0f7b849c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -593,6 +593,77 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b7ab1868", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5.5581057e-24-2.7243789e-24j 8.2296243e-25+6.1320864e-24j\n", + " -6.0873832e-24-1.0901924e-24j ... 0.0000000e+00+0.0000000e+00j\n", + " 0.0000000e+00+0.0000000e+00j 0.0000000e+00+0.0000000e+00j]\n", + "[ 5.55985471e-24-2.72080888e-24j 8.22331359e-25+6.13217153e-24j\n", + " -6.08430890e-24-1.10723009e-24j ... 0.00000000e+00+0.00000000e+00j\n", + " 0.00000000e+00+0.00000000e+00j 0.00000000e+00+0.00000000e+00j]\n" + ] + } + ], + "source": [ + "print(hp_ripple)\n", + "print(hp_lalsuite)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c266abf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43.136898\n", + "43.13691\n", + "nan\n" + ] + } + ], + "source": [ + "psd_file = \"psds/psd.txt\"\n", + "f_ASD, ASD = np.loadtxt(psd_file, unpack=True)\n", + "ASD = np.sqrt(ASD)\n", + "\n", + "ASD_vals = np.interp(fs, f_ASD, ASD)\n", + "PSD_vals = ASD_vals**2\n", + "pad_low, pad_high = get_eff_pads(fs)\n", + "# match = get_match_arr(\n", + "# pad_low,\n", + "# pad_high,\n", + "# PSD_vals,\n", + "# hp_ripple,\n", + "# hp_lalsuite,\n", + "# )\n", + "\n", + "h1s = hp_ripple\n", + "h2s = hp_lalsuite\n", + "\n", + "norm1 = jnp.sqrt(jnp.sum((jnp.abs(h1s)/ASD_vals)**2))\n", + "norm2 = jnp.sqrt(jnp.sum((jnp.abs(h2s)/ASD_vals)**2))\n", + "\n", + "print(norm1)\n", + "print(norm2)\n", + "# Use IFFT trick to maximize over t_c. Ref: Maggiore's book, eq. 7.171.\n", + "integrand_padded = jnp.concatenate((pad_low, h1s.conj() * h2s / PSD_vals, pad_high))\n", + "match = jnp.abs(len(integrand_padded) * jnp.fft.ifft(integrand_padded)).max() / (norm1 * norm2)\n", + "\n", + "print(match)" + ] + }, { "cell_type": "markdown", "id": "b1a7a0d9", @@ -603,13 +674,13 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 20, "id": "5940b64e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -689,13 +760,13 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 21, "id": "ea95f1b6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -755,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 23, "id": "2d659e2e", "metadata": {}, "outputs": [], @@ -810,23 +881,43 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 24, "id": "1bc242c1", "metadata": {}, "outputs": [], "source": [ - "psi_bbh, phase_shift, phifRef, phiTfRef, dphi_merger, psi_T, psi_SS = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" + "psi_bbh, phase_shift, phifRef, phiTfRef, dphiXAS, dphiT, dphi_merger, psi_T, psi_SS = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 25, + "id": "88e0b3b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0024134202\n", + "-0.036544297\n" + ] + } + ], + "source": [ + "print(dphiXAS)\n", + "print(dphiT)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "a7ae5689", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -857,13 +948,13 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 27, "id": "37a2cbc6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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CzhM+Pj4wNjZGcXFxpdc//Xt+/fp1CCHKvEaKqMZJdh8bkR4q76nSQggxevRoAeCZT6AW4vFTez09PYWnp6farepPVPXWeiGEOHLkiGjXrp0wMTERHh4eYvXq1aWedPzo0SMxduxYYWdnJywsLERwcLC4cuWKaNKkiRg9evQz30MIIQ4cOCAACJlMVubjATZt2iSCgoKEo6OjMDIyEvXr1xdBQUFi9+7dZY7naZWtsaw6y6s7MjJS+Pn5CRMTE+Hp6Sm+++47MWPGDGFqaqrq8+Rndf/+/Qrf44m0tDQxceJE4erqKoyNjYWTk5Po1auXWLNmjVq/yhyXsgwYMED1aIKKXsbGxiI9PV3Ex8cLb2/vUutfeOEFce7cObV9Dxo0SMybN08IIUR+fr5YsGCB6NSpk1AqlZVeP3v2bLV9/vrrr6JDhw4VjomopsiEeOp8MhERPVNoaCguXrxY5jU9ukoIgdOnT6N3797o1q0bVq5cWeruOODxnXQpKSkwNjZGTk4OGjZsiCNHjsDNza3S61NTU1VTm8Dj7zkbPnw4vv3229oYKpEaXjNERPQMTy7efiIhIQH79u0r9awdXSeTyeDl5YXs7GyEhISUGYRycnJw7do1xMbGIiMjA7m5uXjxxRfx3nvvVWn93r17ERMTo3p179691LfeE9UWXjNERPQMHh4eGDNmDDw8PHDjxg2sWrUKJiYmqg/4uuRZt9GfP38eVlZW8PT0BPD4kQmBgYH46aefKr3e3NwcXbt2VXsMweXLlzF79uwaGxdRRRiGiIieISQkBJs2bUJqaqrqw33x4sV18mLfp7+T7GmxsbHw8/NTfUltQkICVqxYgVGjRlV6va+vr1oQevjwIZKTk9G2bdsaGxdRRRiGiIieYd26dVKXUGsmTJiACRMmlLs+NjYWx44dg4WFBYyMjODi4oI333wT//rXvyq9vl27dmr7PHv2LJo0aQJra+uaGxhRBXgBNREREek1XkBNREREeo1hiIiIiPQarxl6BqVSibt378LS0lJ1QSARERFpNyEEsrOz4eLiAgODis/9MAw9w927d+Hq6ip1GURERFQNt27dQqNGjSrswzD0DJaWlgAe/zCtrKwkroaIiIgqIysrC66urqrP8YowDD3Dk6kxKysrhiEiIiIdU5lLXHgBNREREek1hiEiIiLSawxDREREpNcYhoiIiEivMQwRERGRXmMYIiIiIr3GMERERER6jWGIiIiI9BrDEBEREek1hiEiIiLSawxDREREpNcYhoiIiEivMQwRERGRJIQQiLycBqVSSFoHwxARERHVuuyCYkzdHINxP5zF6qNJktZiJOm7ExERkd6Ju5OJyZuikZyeC0MDGYwNpD03wzBEREREtUIIgZ9P3sDHey+jSKGEs7UploX5ob2braR1MQwRERFRjcvML8b7v5zH/rhUAEBQSwd8PsQHNuYmElfGMEREREQ1LPZWBiZtisKth/kwNpRhdkgLjOviDplMJnVpABiGiIiIqIYIIbDu7+sI338ZxQqBRvXNsHyEP3xdbaQuTQ3DEBEREWlcRl4RZm47j4OX0wAAIa2d8J/BbWFtZixxZaUxDBEREZFGnbvxCFM2ReNORj5MDA0wt18LjO7spjXTYk9jGCIiIiKNUCoF1vx1DZ//Ho8SpYBbA3MsH+EP74bWUpdWIYYhIiIiem4PcgoxY1ssDsffBwC81NYZ4YPawNJU+6bFnsYwRERERM/ldPJDTN4UhbSsQsiNDDBvQCuM6NhYa6fFnsYwRERERNWiUAqsOpyILw9chVIAHvb1sGKEP1o6W0ldWpUwDBEREVGV3c8uxL+2xOBYYjoAYJBfQ3wc6o16ct2LFrpXMREREUnqeGI6pmyOQXpOIUyNDbDoFW8MaddIZ6bFnsYwRERERJWiUAp8HZmAZX8mQAigmYMFVo70RzNHS6lLey4MQ0RERPRMaVkFmLIpGqeSHwIAhrZvhIUve8PMxFDiyp4fwxARERFV6MjV+5i+JQYPcotgbmKIfw/0xkC/RlKXpTEMQ0RERFSmEoUSXxy4ilWHkwAALZwssWKkPzztLSSuTLMYhoiIiKiUlMx8TNkUjTPXHwEARgQ0xryXWsHUWPenxZ7GMERERERqDl25h+lbY/AorxgWciOED2qDAT4uUpdVYxiGiIiICABQrFDis9/jseboNQBAaxcrrBjhDze7ehJXVrMYhoiIiAi3H+Vh0sZoxNzKAACMDmyCuf1bQm5U96bFnsYwREREpOci4lLx3vZYZBWUwNLUCJ8NbosQb2epy6o1DENERER6qqhEiSX7r2Dt38kAAB9XGywP84OrrbnEldUuhiEiIiI9dPNBHiZtisL525kAgHFd3DE7pAVMjAwkrqz2MQwRERHpmX0XUjD7l/PILiiBtZkxPh/ig96tHKUuSzIMQ0RERHqisESBf/92GT+euAEA8G9sg6VhfmhUX7+mxZ7GMERERKQHktNzMXFDFC6lZAEA3u7ugZl9vGBsqH/TYk/Tmp/A0aNHMWDAALi4uEAmk2HXrl1q68eMGQOZTKb2CgkJeeZ+V6xYATc3N5iamiIgIACnT5+uoREQERFpp90xd/DS0r9wKSUL9c2NsW5sB8zp25JB6P9pzU8hNzcXPj4+WLFiRbl9QkJCkJKSonpt2rSpwn1u2bIF06dPx/z58xEVFQUfHx8EBwfj3r17mi6fiIhI6+QXKTBnx3lM3RyD3CIFOrrbYv/Ubujp5SB1aVpFa6bJ+vbti759+1bYRy6Xw8nJqdL7/PLLL/HWW29h7NixAIDVq1fjt99+w9q1a/H+++8/V71ERETaLPFeNiZtjMaV1GzIZMCknk0xtVczGPFsUCk69RM5fPgwHBwc4OXlhQkTJuDBgwfl9i0qKsK5c+cQFBSkajMwMEBQUBBOnDhR7naFhYXIyspSexEREemSX87dxsvL/8aV1GzYWZjgpzcCMKOPF4NQObTmzNCzhISEYNCgQXB3d0dSUhLmzp2Lvn374sSJEzA0LP2o8PT0dCgUCjg6qt8q6OjoiCtXrpT7PuHh4Vi4cKHG6yciIqppeUUl+GjXRfwSdRsA0NmzAb4a7gsHS1OJK9NuOhOGhg8frvp3mzZt0LZtW3h6euLw4cPo1auXxt5nzpw5mD59umo5KysLrq6uGts/ERFRTYhPzcbEjVFIvJcDAxkwtVdzTHqxKQwNZFKXpvV0Jgw9zcPDA3Z2dkhMTCwzDNnZ2cHQ0BBpaWlq7WlpaRVedySXyyGXyzVeLxERUU0QQmDr2VuYv+ciCoqVcLCU4+vhfgj0bCB1aTpDZycPb9++jQcPHsDZuewvkjMxMUG7du0QGRmpalMqlYiMjERgYGBtlUlERFRjcgpLMG1LDGb/cgEFxUp0bWaHfVO7MghVkdacGcrJyUFiYqJqOTk5GTExMbC1tYWtrS0WLlyIV199FU5OTkhKSsJ7772Hpk2bIjg4WLVNr169MHDgQEyaNAkAMH36dIwePRrt27dHx44d8dVXXyE3N1d1dxkREZGuung3E5M2RiM5PReGBjJM790cE7p7woDTYlWmNWHo7Nmz6Nmzp2r5yXU7o0ePxqpVq3D+/Hn88MMPyMjIgIuLC/r06YOPP/5YbUorKSkJ6enpquVhw4bh/v37mDdvHlJTU+Hr64uIiIhSF1UTERHpCiEEfjp5A5/svYwihRLO1qZYFuaH9m62Upems2RCCCF1EdosKysL1tbWyMzMhJWVldTlEBGRHsvML8acHeex70IqACCopQM+H+IDG3MTiSvTPlX5/NaaM0NERERUvthbGZi0KQq3HubD2FCG2SEtMK6LO2QyTos9L4YhIiIiLSaEwLq/ryN8/2UUKwQa1TfD8hH+8HW1kbq0OoNhiIiISEtl5hXjvV9i8fvFx4+JCWnthP8MbgtrM2OJK6tbGIaIiIi0UNTNR5i8MRp3Mh5Pi33QryVGd3bjtFgNYBgiIiLSIkIIfPvXNXwaEY8SpUBjW3OsGOGPNo2spS6tzmIYIiIi0hIPc4swY2sMDsXfBwC81NYZ4YPawNKU02I1iWGIiIhIC5xOfoipm6ORklkAEyMDzHupFUYGNOa0WC1gGCIiIpKQQimw+kgSvvgjHkoBeNjVw/IR/mjlwmfb1RaGISIiIonczy7E9K0x+Cvh8bcnhPq64N8D26CenB/PtYk/bSIiIgkcT0rH1M0xuJ9dCFNjAyx62RtD2jfitJgEGIaIiIhqkUIpsDQyAUv/TIAQQDMHC6wY6Y/mjpZSl6a3GIaIiIhqyb2sAkzdHIMT1x4AAIa0a4SFr7SGuQk/jqXEnz4REVEtOHr1PqZvjUF6ThHMTQzxSag3Bvk3krosAsMQERFRjSpRKPHfg1ex8nAShABaOFli+Qh/NHWwkLo0+n8MQ0RERDUkJTMfUzZF48z1RwCAEQGNMe+lVjA1NpS4MvonhiEiIqIa8OeVNMzYGotHecWwkBth8aA2eNnHReqyqAwMQ0RERBpUVKLEZ79fwbd/JQMAWrtYYcUIf7jZ1ZO4MioPwxAREZGG3H6Uh0kboxFzKwMAMKazG+b0awG5EafFtBnDEBERkQb8fjEVs7bFIqugBJamRvhssA9CvJ2kLosqgWGIiIjoORSWKLBk/xWs+/s6AMDH1QbLw/zgamsubWFUaQxDRERE1XTjQS4mb4rG+duZAIA3u7jjvZAWMDEykLgyqgqGISIiomrYdyEFs7efR3ZhCWzMjfH5YB8EtXKUuiyqBoYhIiKiKigoVuCT3y7h55M3AQDtmtTH0jA/NLQxk7gyqi6GISIiokq6dj8HEzdG43JKFgBgQg9PTO/dHMaGnBbTZQxDRERElbA75g7m7riA3CIFGtQzwRdDfdDDy0HqskgDGIaIiIgqkF+kwKK9F7Hp9C0AQEd3Wywd7gcna1OJKyNNYRgiIiIqR0JaNiZtjEZ8WjZkMmBSz6aY2qsZjDgtVqcwDBEREZVh+7nb+GhXHPKLFbCzkOPr4b54oamd1GVRDWAYIiIi+oe8ohJ8uCsOO6LuAAA6ezbAV8N94WDJabG6imGIiIjo/11JzcK7G6Jw7X4uDGTAtKDmmNizKQwNZFKXRjWIYYiIiPSeEAKbz9zCgj0XUViihKOVHF8P90MnjwZSl0a1gGGIiIj0Wk5hCebuuIA9sXcBAN2a2+O/Q33QwEIucWVUWxiGiIhIb126m4WJG6OQnJ4LQwMZZgV7YXxXDxhwWkyvMAwREZHeEULg51M38fHeSygqUcLZ2hTLwvzQ3s1W6tJIAgxDRESkV7IKijHnlwv47UIKAODFFg74fIgPbOuZSFwZSYVhiIiI9Mb52xmYtDEaNx/mwchAhtkhLfBmV3fIZJwW02cMQ0REVOcJIbD++HWE77uCIoUSDW3MsGyEH/wb15e6NNICDENERFSnZeYVY+b2WBy4lAYA6NPKEZ8N9oG1ubHElZG2YBgiIqI669yNR5iyKRp3MvJhbCjD3H4tMaazG6fFSA3DEBER1TlKpcB3x67h04h4lCgFmjQwx/Iwf7RpZC11aaSFtOZrd48ePYoBAwbAxcUFMpkMu3btUq0rLi7G7Nmz0aZNG9SrVw8uLi4YNWoU7t69W+E+FyxYAJlMpvZq0aJFDY+EiIik9DC3CG/8cAaL911BiVKgf1tn/Dq5C4MQlUtrwlBubi58fHywYsWKUuvy8vIQFRWFjz76CFFRUdixYwfi4+Px8ssvP3O/rVu3RkpKiup17NixmiifiIi0wKlrD9D366M4HH8fJkYG+CTUG8vD/GBlyuuDqHxaM03Wt29f9O3bt8x11tbWOHDggFrb8uXL0bFjR9y8eRONGzcud79GRkZwcnLSaK1ERKRdFEqBVYcT8eWBq1AKwMOuHla+5o8WTlZSl0Y6QGvCUFVlZmZCJpPBxsamwn4JCQlwcXGBqakpAgMDER4eXmF4KiwsRGFhoWo5KytLUyUTEVENuJddgOlbYnEsMR0AMNCvIT4J9UY9uc5+xFEt05ppsqooKCjA7NmzERYWBiur8lN/QEAA1q9fj4iICKxatQrJycno2rUrsrOzy90mPDwc1tbWqperq2tNDIGIiDTgeGI6+i89hmOJ6TAzNsSng9viv8N8GYSoSmRCCCF1EU+TyWTYuXMnQkNDS60rLi7Gq6++itu3b+Pw4cMVhqGnZWRkoEmTJvjyyy8xbty4MvuUdWbI1dUVmZmZVXovIiKqOSUKJZb+mYhlfyZACKC5owWWj/BHc0dLqUsjLZGVlQVra+tKfX7rVHQuLi7G0KFDcePGDfz5559VDic2NjZo3rw5EhMTy+0jl8shl8uft1QiIqohaVkFmLo5GievPQQADG3fCAtf9oaZiaHElZGu0plpsidBKCEhAQcPHkSDBg2qvI+cnBwkJSXB2dm5BiokIqKaduTqffT7+i+cvPYQ5iaG+O8wH3w62IdBiJ6L1pwZysnJUTtjk5ycjJiYGNja2sLZ2RmDBw9GVFQU9u7dC4VCgdTUVACAra0tTEwef9Nwr169MHDgQEyaNAkAMHPmTAwYMABNmjTB3bt3MX/+fBgaGiIsLKz2B0hERNVWolDiywNXsepIEoQAWjhZYsVIf3jaW0hdGtUBWhOGzp49i549e6qWp0+fDgAYPXo0FixYgD179gAAfH191bY7dOgQevToAQBISkpCenq6at3t27cRFhaGBw8ewN7eHl26dMHJkydhb29fs4MhIiKNuZuRj8mbonHuxiMAwIiAxpj3UiuYGvNsEGmGVl5ArU2qcgEWERFp1sFLaZi5PRYZecWwlBsh/NU2eKmti9RlkQ6osxdQExGRfihWKPFpxBV8+1cyAMC7oRVWjPBHkwb1JK6M6iKGISIi0iq3HuZh8qZoxNzKAACM6eyGOf1aQG7EaTGqGQxDRESkNSLiUjFrWyyyC0tgZWqETwf7IMSbX6lENYthiIiIJFdYokD4vitYf/w6AMCnkTWWj/CHq625tIWRXmAYIiIiSV1Pz8XEjVG4ePfxd0G+2cUds/u2gLGhzjwKj3QcwxAREUlm7/m7eP+XC8gpLIG1mTG+HOqDXi0dpS6L9AzDEBER1bqCYgU+3nsJG07dBAC0a1IfS8P80NDGTOLKSB8xDBERUa26dj8HEzdG43LK42mxd7p7Ykaf5pwWI8kwDBERUa3ZGX0bH+6MQ26RArb1TPDlUB/08HKQuizScwxDRERU4/KKSjB/90VsO3cbANDR3RbLwvzgaGUqcWVEDENERFTDEtKyMXFjFK6m5UAmA6a82AxTejWDoYFM6tKIADAMERFRDdp29hY+2h2HgmIl7C3l+Hq4Lzp72kldFpEahiEiItK43MISfLQrDjui7wAAujazwxdDfeBgyWkx0j4MQ0REpFFXUrPw7oYoXLufCwMZ8K+g5ni3Z1NOi5HWYhgiIiKNEEJg0+lbWPjrRRSWKOFoJcfS4X4I8GggdWlEFWIYIiKi55ZdUIy5O+Pwa+xdAEAPL3t8McQHDSzkEldG9GwMQ0RE9Fzi7mRi0sYoXH+QB0MDGWYFe2F8Vw8YcFqMdATDEBERVYsQAlvO3ML8PY+nxVysTbFshB/aNbGVujSiKmEYIiKiKssuKMYHO+Ow5x/TYl8N84WNuYnElRFVHcMQERFVydPTYjP7eOHtbpwWI93FMERERJUihMBPJ2/gk72XUaR4PC22NMwP7d04LUa6jWGIiIieKTO/GLO3n0fExVQAQFBLB3w+xIfTYlQnMAwREVGFYm9lYNKmKNx6mA9jQxlmh7TAuC7ukMk4LUZ1A8MQERGVSQiBdX9fR/j+yyhWCDSqb4YVI/zh42ojdWlEGsUwREREpWTkFWHW9vM4cCkNABDS2gn/GdwW1mbGEldGpHkMQ0REpObcjUeYvDEKdzMLYGJogLn9WmB0ZzdOi1GdxTBEREQAAKVS4Nu/ruGz3+NRohRwa2CO5SP84d3QWurSiGoUwxAREeFhbhGmb43B4fj7AIABPi5YPNAblqacFqO6j2GIiEjPnbr2AFM2RyMtqxByIwPMG9AKIzo25rQY6Q2GISIiPaVUCqw6koQv/oiHUgCe9vWwLMwfrVyspC6NqFYxDBER6aH72YWYvjUGfyWkAwAG+jXEJ6HeqCfnxwLpH/7WExHpmeNJ6Zi6OQb3swthamyARS97Y0j7RpwWI73FMEREpCcUSoGlkQlY+mcChACaOVhg5Uh/NHO0lLo0IkkxDBER6YF7WQWYsjkaJ689BAAMbd8IC15uDXMTfgwQ8a+AiKiOO3L1PmZsjUF6ThHMTQzx74HeGOjXSOqyiLQGwxARUR1VolDiywNXsfJwEgCghZMlVoz0h6e9hcSVEWkXhiEiojrobkY+pm6OxpnrjwAAIwIaY95LrWBqbChxZUTah2GIiKiOibychhnbYpGRVwwLuRGWvNoGL7V1kbosIq3FMEREVEcUK5T4NOIKvv0rGQDQpqE1lo/wQ5MG9SSujEi7MQwREdUBtx7mYdKmaMTeygAAjOnshjn9WkBuxGkxomcxkLqAJ44ePYoBAwbAxcUFMpkMu3btUlsvhMC8efPg7OwMMzMzBAUFISEh4Zn7XbFiBdzc3GBqaoqAgACcPn26hkZARCSNiLgU9Fv6F2JvZcDK1AirX2uHBS+3ZhAiqiStCUO5ubnw8fHBihUrylz/6aefYunSpVi9ejVOnTqFevXqITg4GAUFBeXuc8uWLZg+fTrmz5+PqKgo+Pj4IDg4GPfu3aupYRAR1ZrCEgUW7LmId36OQnZBCXxdbfDblK4I8XaSujQinSITQgipi3iaTCbDzp07ERoaCuDxWSEXFxfMmDEDM2fOBABkZmbC0dER69evx/Dhw8vcT0BAADp06IDly5cDAJRKJVxdXTF58mS8//77laolKysL1tbWyMzMhJUVv7yQiLTD9fRcTNoUhbg7WQCA8d08MCvYC8aGWvP/uESSqsrnt0781SQnJyM1NRVBQUGqNmtrawQEBODEiRNlblNUVIRz586pbWNgYICgoKBytwGAwsJCZGVlqb2IiLTJr7F38dKyY4i7kwUbc2N8P7o95vZrySBEVE068ZeTmpoKAHB0dFRrd3R0VK17Wnp6OhQKRZW2AYDw8HBYW1urXq6urs9ZPRGRZhQUKzB35wVM3hSNnMIStG9SH/umdEWvlo7P3piIyqUTYag2zZkzB5mZmarXrVu3pC6JiAiJ97IRuuJvbDx1EwAwsacnNo/vBBcbM4krI9J9OnFrvZPT44sB09LS4OzsrGpPS0uDr69vmdvY2dnB0NAQaWlpau1paWmq/ZVFLpdDLpc/f9FERBqyK/oO5u68gLwiBewsTPDlUF90a24vdVlEdYZOnBlyd3eHk5MTIiMjVW1ZWVk4deoUAgMDy9zGxMQE7dq1U9tGqVQiMjKy3G2IiLRJXlEJZm2LxbQtMcgrUiDQowH2TenKIESkYVpzZignJweJiYmq5eTkZMTExMDW1haNGzfGtGnT8Mknn6BZs2Zwd3fHRx99BBcXF9UdZwDQq1cvDBw4EJMmTQIATJ8+HaNHj0b79u3RsWNHfPXVV8jNzcXYsWNre3hERFVyNS0bkzZG4WpaDgxkwKQXm2HKi01hxIukiTROa8LQ2bNn0bNnT9Xy9OnTAQCjR4/G+vXr8d577yE3Nxfjx49HRkYGunTpgoiICJiamqq2SUpKQnp6ump52LBhuH//PubNm4fU1FT4+voiIiKi1EXVRETaQgiBbWdvY96eOBQUK2FnIcfS4b7o3NRO6tKI6iytfM6QNuFzhoiotuQWluCjXXHYEX0HANClqR3+O8wX9pa8jpGoqqry+a01Z4aIiPTZ5ZQsTNwYhWv3c2EgA6b3bo53ezSFgYFM6tKI6jyGISIiCQkhsPH0TSz69RIKS5RwsjLF18N9EeDRQOrSiPQGwxARkURyCkvw/i/nsfd8CgCgh5c9vhjigwYWnBYjqk0MQ0REEoi7k4lJG6Nw/UEejAxkmBnshfFdPTgtRiQBhiEiolokhMDPJ2/g472XUaRQwsXaFMtG+KFdE1upSyPSWwxDRES1JDO/GO//ch774x5/P2JQSwd8PsQHNuYmEldGpN8YhoiIakHsrQxM2hSFWw/zYWQgw/t9W2BcF3fIZJwWI5IawxARUQ0SQmDt39exZP9lFCsEGtU3w/IR/vB1tZG6NCL6fwxDREQ1JCOvCDO3ncfBy4+/MDqktRP+M7gtrM2MJa6MiP6JYYiIqAacu/EIkzdG4W5mAUwMDfBB/5YYFdiE02JEWohhiIhIg5RKgTV/XcNnv8dDoRRwa2COZWH+aNPIWurSiKgcDENERBryIKcQM7bF4nD8fQDAAB8XLB7oDUtTTosRaTOGISIiDTh17QGmbo5BalYB5EYGmD+gNcI6unJajEgHMAwRET0HpVJg+aFEfHXwKpQC8LCvhxUj/NHSueJvySYi7cEwRERUTfezC/GvLTE4lpgOABjk1xAfh3qjnpz/aSXSJfyLJSKqhr8T0zFtSwzuZxfCzNgQi15pjSHtXaUui4iqgWGIiKgKShRKLI1MwLJDiRACaOZggRUj/dHc0VLq0oiomhiGiIgqKTWzAFM2R+N08kMAwPAOrpg/oDXMTAwlroyIngfDEBFRJRy5eh//2hKDh7lFqGdiiMWD2uAV34ZSl0VEGsAwRERUgWKFEl8euIpVh5MAAK2crbB8hB887C0kroyINKVaYeiPP/6Ar68vHBwcNF0PEZHWuJuRj0kboxB1MwMAMDKgMT56qRVMjTktRlSXVCsMhYaGorCwEI6OjvD19YWfnx969+6NHj16aLg8IiJpHLyUhhnbYpGZXwxLUyMsGdQW/ds6S10WEdWAaoWh7OxsXL58GbGxsYiNjcWpU6fwxRdfIDAwEHv37kW9evU0XScRUa0oKlHiPxFX8P2xZABAm4bWWD7CD00a8L9rRHWVTAghqrrRvXv3Sk2R3bt3D4MGDUKXLl2wZMkSjRUotaysLFhbWyMzMxNWVnyiLFFdduthHiZtikbsrQwAwJjObpjbryVMjAykLYyIqqwqn9/VCkMGBgZwcnKCj4+P6uXr64vc3Fz069cP9+7dq3bx2oZhiEg/RMSlYNb288guKIGVqRE+HeyDEG8nqcsiomqqyud3tabJEhISEBsbi/PnzyM2NhZbt27F9evXYWJiguLiYrz22msICAiAr68vunbtWq1BEBHVhsISBRb/dhk/nLgBAPB1tcGyMD+42ppLXBkR1ZZqnRkqS1ZWFg4dOoSBAwciLCwM8fHxuHTpEvLy8jSxe8nwzBBR3XXjQS4mboxC3J0sAMD4bh6Y2ceL02JEdUCNnxmytbWFr6+vaoqsTZs2sLCwwL59++Dp6YkNGzYAABQKRXV2T0RU4/bE3sXcHReQU1iC+ubG+GKoD15s4Sh1WUQkgWqFobVr16ruJNu9ezeuX78OADAzM8O2bdtU/QwN+SwOItIuBcUKLPz1EjadvgkAaN+kPpaG+cHFxkziyohIKtV+zlBoaKhqOTs7GykpKWjYsCGSk5M1VRsRkUYl3c/BpI3RuJySBZkMmNijKaYFNYORIafFiPSZxr6O4/Dhw/j+++9x7tw5lJSUaGq3REQasSv6DubuvIC8IgVs65ngq2G+6NbcXuqyiEgLPFcYOnr0KL7//nv88ssvMDc3R9euXXH27FlN1UZE9NzyixSYvycOW8/eBgB08rDF0uF+cLAylbgyItIWVT43nJqaiiVLlqBZs2bo168fSkpKsHXrVty9excLFy6siRqJiKolIS0bLy8/hq1nb0MmA6b0aoYNb3ZiECIiNVU6MzRgwABERkaiZ8+eWLBgAUJDQ9W+ekMmk2m8QCKi6th29hbm7b6I/GIF7C3l+GqYL15oaid1WUSkhaoUhn777TeMGDEC06ZNQ/v27WuqJiKiasstLMFHu+KwI/oOAKBLUzv8d5gv7C3lEldGRNqqStNkx48fh5mZGV588UV4eXlh0aJFSEpKqqnaiIiq5EpqFl5efgw7ou/AQAbM6N0cP77RkUGIiCpUrSdQ5+bmYsuWLVi7di1OnDiBDh06YOTIkWjdujV69+5dpx62yCdQE2k/IQQ2n7mFBXsuorBECUcrOZaF+aOju63UpRGRRGr8i1r/KT4+Ht9//z1++uknpKWlQSaTMQwRUa3JLijG3J1x+DX2LgCge3N7fDnUBw0seDaISJ/Vahh6QqFQ4Ndff8XatWuxZ88eTexSKzAMEWmvC7czMWlTFG48yIOhgQwz+3jh7W4eMDDgzRxE+k6SMFRXMQwRaR8hBH44fh2L911BkUKJhjZmWBrmh3ZN6ktdGhFpiap8fuvUM+jd3Nwgk8lKvSZOnFhm//Xr15fqa2rK54sQ6bLMvGJM+DkKC369hCKFEr1bOeK3KV0YhIio2jT2dRy14cyZM2rXI8XFxaF3794YMmRIudtYWVkhPj5etcxnIRHpruibjzB5UzRuP8qHsaEM7/dtiTdecOPfNRE9F50KQ/b26t8jtGTJEnh6eqJ79+7lbiOTyeDk5FTTpRFRDVIqBb47dg2fRsSjRCngamuG5WH+8HG1kbo0IqoDdCoM/VNRURF+/vlnTJ8+vcL/K8zJyUGTJk2gVCrh7++PxYsXo3Xr1uX2LywsRGFhoWo5KytLo3UTUdWkZRVg0MrjuJORDwDo38YZ4a+2gZWpscSVEVFdoVPXDP3Trl27kJGRgTFjxpTbx8vLC2vXrsXu3bvx888/Q6lUonPnzrh9+3a524SHh8Pa2lr1cnV1rYHqiagyzt14iN5fHlEFoY9DvbF8hB+DEBFplM7eTRYcHAwTExP8+uuvld6muLgYLVu2RFhYGD7++OMy+5R1ZsjV1ZV3kxHVIqVS4Juj1/D5H/FQKB//J2rv5C7wbmgtcWVEpCuqcjeZTk6T3bhxAwcPHsSOHTuqtJ2xsTH8/PyQmJhYbh+5XA65nA9rI5LKg5xCTN8aiyNX7wMAXvZxwb8HesOSZ4OIqIboZBhat24dHBwc0L9//yptp1AocOHCBfTr16+GKiOi53Hy2gNM3RyNtKxCyI0MsODl1hjewZV3ixFRjdK5MKRUKrFu3TqMHj0aRkbq5Y8aNQoNGzZEeHg4AGDRokXo1KkTmjZtioyMDHz22We4ceMG3nzzTSlKJ6JyKJQCqw4n4ssDV6EUgKd9PawY6Y8WTpyaJqKap3Nh6ODBg7h58ybeeOONUutu3rwJA4P/XRP+6NEjvPXWW0hNTUX9+vXRrl07HD9+HK1atarNkomoAvezC9Hh3wdVy4P8G+LjV7xRT65z/3kiIh2lsxdQ1xZ+HQdRzdl65hbe++U8AMDU2AAfv+KNIe15BycRPb86fwE1Eem2EoUSH+2Ow6bTt1Rtm8cHwpcPUSQiCTAMEVGtSs0swJTN0Tid/BAA4N/YBhve7AQzE0OJKyMifcUwRES15lD8PczYGouHuUUAgEk9m2JmsJfEVRGRvmMYIqIaV6xQ4os/rmL1kSQAQCtnKywf4QcPewuJKyMiYhgiohp2JyMfUzZF49yNRwCA1zo1xof9W8HUmNNiRKQdGIaIqMYcvJSGmdtjkZFXDEu5EZa82hb92zpLXRYRkRqGISLSuKISJf4TcQXfH0sGALRtZI3lYf5o3MBc4sqIiEpjGCIijUpIy0bv/x5VLY99wQ1z+raEiZFBBVsREUmHYYiINGZH1G1M3xqrWl7zejv0ae0kYUVERM/GMEREz62wRIHQFcdxOSVL1bZvSle0cuFT24lI+zEMEdFzuZ6ei3d+Pocrqdmqtisfh/BuMSLSGQxDRFRte2LvYu6OC8gpLAEAhA9qg7COjSWuioioahiGiKjKCooVWPjrJWw6fRMA0MGtPpaG+cHZ2kziyoiIqo5hiIiqJPFeDiZtjMKV1GzIZMDEHk0xLagZjAx5txgR6SaGISKqtNe/P4W/EtIBAHYWJvjvMF90bWYvcVVERM+HYYiInim/SIHxP51VBaFAjwb4ergvHKxMJa6MiOj5MQwRUYWOJ6VjxLenVMveDa3w85sBMDSQSVgVEZHmMAwRUZmEEPg6MgFfHUxQtX051AeD/BtJWBURkeYxDBFRKTmFJfhg5wXsjrmrajs5pxecrDktRkR1D8MQEam5eDcTkzZGIzk9FwDQrkl9bH07kNNiRFRnMQwREYDH02KzfzmPrWdvAwCcrU2xNMwPHdxsJa6MiKhmMQwREbIKitF2wR+q5V4tHPDZEB/Y1jORsCoiotrBMESk544npuONH86olse+4IaP+reCAafFiEhPMAwR6SkhBF784ojq2iAA+HRwWwxt7yphVUREtY9hiEgPZeYVY9b2WFUQ8nK0xNa3A2FtbixxZUREtY9hiEjPRN98hEkbo3EnI1/VFjGtK2QyTosRkX5iGCLSE0qlwPifzuLg5XsAAFdbM6wY4Y+2jWykLYyISGIMQ0R64NbDPHT99JBquV8bJyx5tS2sTDktRkTEMERUx608nIhPI+JVyy+2cMCKEf6cFiMi+n8MQ0R1lFIpEL7/Mr79K1nVtmBAK4x5wV3CqoiItA/DEFEdlJ5TiH9ticFfCemqttMf9IKDJb9bjIjoaQxDRHXMzujbmLE1FkrxeLmvtxNWjuS0GBFReRiGiOoIhVLAc+4+1XJTBwusGOEPLydLCasiItJ+DENEdcDdjHx0XvKnarmFkyV2vNsZ5ib8Eyciehb+l5JIx/2VcB9v/XhWrS1iWjeJqiEi0j0MQ0Q6qqhEiX5L/0LivRxV26qR/ujbxlnCqoiIdA/DEJEOirmVgdAVf6uWwzo2xvwBrWBqbChhVUREuolhiEjHfBpxBSsPJ6mW/z3QGyMDmkhYERGRbmMYItIRxQolPv89Ht8cvaZq+2VCINo1sZWwKiIi3WcgdQGVtWDBAshkMrVXixYtKtxm27ZtaNGiBUxNTdGmTRvs27evwv5E2irxXjaafbBfLQhdWhTMIEREpAE6dWaodevWOHjwoGrZyKj88o8fP46wsDCEh4fjpZdewsaNGxEaGoqoqCh4e3vXRrlEGjHh53PYH5eqWuZF0kREmqVTYcjIyAhOTk6V6vv1118jJCQEs2bNAgB8/PHHOHDgAJYvX47Vq1fXZJlEGlFYosCCPRfVgtDuiS/Ax9VGuqKIiOognZkmA4CEhAS4uLjAw8MDI0eOxM2bN8vte+LECQQFBam1BQcH48SJEzVdJtFzS7qfg1bzfsem07dUbZcWBTMIERHVAJ05MxQQEID169fDy8sLKSkpWLhwIbp27Yq4uDhYWpb+uoHU1FQ4OjqqtTk6OiI1NbVU338qLCxEYWGhajkrK0szAyCqpNe/P6X2BatrXm+HPq0rd0aUiIiqTmfCUN++fVX/btu2LQICAtCkSRNs3boV48aN09j7hIeHY+HChRrbH1FlFRQr8O/fLqsFob/e6wlXW3MJqyIiqvt0aprsn2xsbNC8eXMkJiaWud7JyQlpaWlqbWlpac+85mjOnDnIzMxUvW7dulVhfyJNiLr5CC0+isBPJ2+o2uI/CWEQIiKqBTobhnJycpCUlARn57LvqgkMDERkZKRa24EDBxAYGFjhfuVyOaysrNReRDVpxLcnMWjlcdXyurEdcH1Jf8iN+DRpIqLaoDPTZDNnzsSAAQPQpEkT3L17F/Pnz4ehoSHCwsIAAKNGjULDhg0RHh4OAJg6dSq6d++OL774Av3798fmzZtx9uxZrFmzRsphEKlkFRSj7YI/1Nr2THoBbRvZSFMQEZGe0pkwdPv2bYSFheHBgwewt7dHly5dcPLkSdjb2wMAbt68CQOD/53o6ty5MzZu3IgPP/wQc+fORbNmzbBr1y4+Y4i0wu8XU/H2T+fU2uI/CeHZICIiCciEEELqIrRZVlYWrK2tkZmZySkz0ohfzt3GjG2xquVPX22LoR1cJayIiKjuqcrnt86cGSLSdZl5xfBZpD4ttndyF3g3tJaoIiIiAhiGiGpFfGo2gr86qlqe0MMTM/t4wdBAJmFVREQEMAwR1SghBFrN+x35xQpV26v+jTA7pOIvGSYiotrDMERUQ3IKS9D+kwMoKFaq2g78qxuaOZZ+YjoREUmHYYioBmw5cxOzf7mg1nZtcT8YcFqMiEjrMAwRaZAQAl4fRqBI8b+zQe/3bYF3untKWBUREVWEYYhIQzLzi+GzUP1uscgZ3eFpbyFRRUREVBkMQ0Qa8N8DV/F1ZIJq2dfVBjvf7QyZjNNiRETajmGI6DkIIfDe9vPYdu62qm1Kr2aY3ru5hFUREVFVMAwRVdPdjHx0XvKnWtvpub3gYGUqUUVERFQdDENE1TB/dxx+OHFDtWwpN8L5BX04LUZEpIMYhoiqQAiBt386hz8upanapvdujim9mklYFRERPQ+GIaJKKutusZh5vWFjbiJRRUREpAkMQ0SVEBGXgnd+jlItt3K2wm9TunBajIioDmAYIqqAUingMXefWtuiV1pjVKCbNAUREZHGMQwRlePpb5oHgNMf9IKDJe8WIyKqSxiGiMrwn4grWHU4SbXsblcPf87ozmkxIqI6iGGI6B8USgHPp6bF9k7uAu+G1hJVRERENY1hiOj/JaRlo/d/1afFoj7qDdt6vFuMiKguYxgiAhC+7zK+OXpNtVzf3BjR8/pIWBEREdUWhiHSawqlQOclkUjLKlS1bXwzAJ2b2klYFRER1SaGIdJbyem56Pn5YbU2PkSRiEj/MAyRXnrnp3OIuJiqWnZrYI7Ds3pKWBEREUmFYYj0SolCiaYf7FdrWznSH/3aOEtUERERSY1hiPRG3J1MvLTsmFrb+QV9YGVqLFFFRESkDRiGSC98svcSvjuWrFr2crTE7//qJmFFRESkLRiGqE4rKFagxUcRam3fjmqP3q0cJaqIiIi0DcMQ1Vn7L6RgwoYotbbY+X1gbcZpMSIi+h+GIaqTvjp4FV8dTFAtv9apMT4JbSNhRUREpK0YhqhOKWta7OdxAejSjA9RJCKisjEMUZ3xd2I6Rn53Sq3t0qJgmJvw15yIiMrHTwmqE1rPi0BukUK13L5JfWyf0FnCioiISFcwDJFOyy9SoOU89Wmx7e8Eor2brUQVERGRrmEYIp21O+YOpm6OUWu78nEITI0NpSmIiIh0EsMQ6aSh35zA6eSHquXOng2w8a1OElZERES6imGIdEpeUQlazftdrW3r24Ho6M5pMSIiqh6GIdIZv5y7jRnbYtXaEv7dF8aGBhJVREREdQHDEOmErw8m4L8Hr6qWG9ua4+h7PSWsiIiI6gqGIdJq97IK0HFxpFrb/qld0dLZSqKKiIiormEYIq219cwtvPfLedWynYUJTs7pBSNOixERkQbpzKdKeHg4OnToAEtLSzg4OCA0NBTx8fEVbrN+/XrIZDK1l6mpaS1VTNUlhIDb+7+pBaExnd1w9sPeDEJERKRxOnNm6MiRI5g4cSI6dOiAkpISzJ07F3369MGlS5dQr169crezsrJSC00ymaw2yqVqepRbBL+PD6i1cVqMiIhqks6EoYgI9acMr1+/Hg4ODjh37hy6detW7nYymQxOTk41XR5pwIZTN/DBzji1tmuL+8HAgAGWiIhqjs6EoadlZmYCAGxtK36+TE5ODpo0aQKlUgl/f38sXrwYrVu3Lrd/YWEhCgsLVctZWVmaKZgq1Gb+78guLFEtzw5pgQk9PCWsiIiI9IVOXoChVCoxbdo0vPDCC/D29i63n5eXF9auXYvdu3fj559/hlKpROfOnXH79u1ytwkPD4e1tbXq5erqWhNDoP+XkpkPt/d/UwtC+6d2ZRAiIqJaIxNCCKmLqKoJEyZg//79OHbsGBo1alTp7YqLi9GyZUuEhYXh448/LrNPWWeGXF1dkZmZCSsrXreiSd/9dQ2f/HZZrS05vB+v6yIioueWlZUFa2vrSn1+69w02aRJk7B3714cPXq0SkEIAIyNjeHn54fExMRy+8jlcsjl8uctkyoghECHfx9Eek6Rqm1G7+aY3KuZhFUREZG+0pkwJITA5MmTsXPnThw+fBju7u5V3odCocCFCxfQr1+/GqiQKuN+diE6/PugWtux2T3RqL65RBUREZG+05kwNHHiRGzcuBG7d++GpaUlUlNTAQDW1tYwMzMDAIwaNQoNGzZEeHg4AGDRokXo1KkTmjZtioyMDHz22We4ceMG3nzzTcnGoc9+jb2LyZuiVctWpkaInd+H02JERCQpnQlDq1atAgD06NFDrX3dunUYM2YMAODmzZswMPjfNeGPHj3CW2+9hdTUVNSvXx/t2rXD8ePH0apVq9oqm/D4rN6KQ4n4/I//fbfYggGtMOaFqp/dIyIi0jSdvIC6NlXlAiwq7VFuEWZtj8XBy/dUbafm9oKjFZ8ETkRENadOX0BNuuN08kNM3RyNlMwCGBrIMKazGz7s35LTYkREpFUYhkjjlEqBoP8ewbX7uQAAd7t6WD7CD61drCWujIiIqDSGIdKopPs56PXFEdXyS22dseTVtrCQ81eNiIi0Ez+hSGNmbovF9nP/e7r3O909MTvEi9NiRESk1RiG6LmVKJRo+sF+tbavh/viFd+GElVERERUeQxD9FzO387Ay8v/VmuLmdcbNuYmElVERERUNQxDVG2DVv6NqJsZqmV3u3o4NLOHZPUQERFVB8MQVVmJQomgL4/g+oM8VdvSMD+87OMiYVVERETVwzBEVXI3Ix8TN0apBaHzC/rAytRYwqqIiIiqj2GIKu3fv13Ct38lq5a7NrPDT+MCJKyIiIjo+TEM0TMVlijg9WGEatlABvw5owfc7OpJWBUREZFmMAxRhWJvZeCVFep3i8UtDIa5CX91iIiobuAnGpWL02JERKQPGIaolIJiBVp8FKHWtnyEH15qy7vFiIio7mEYIjVnrj/EkNUn1NqufBwCU2NDiSoiIiKqWQxDpNJ2we/IKihRLf/n1TYY1qGxhBURERHVPIYhQkGxAq9/f0otCK0d0x4vtnCUsCoiIqLawTCk5yLiUvHOz+fU2jgtRkRE+oRhSI+tOJSIz36PVy1/HOqN1zs1kbAiIiKi2scwpIdyC0vQev7vam3b3wlEezdbiSoiIiKSDsOQntl0+ibm7LigWh7ewRWfhHrDyNBAwqqIiIikwzCkR4Z+cwKnkx+qlod3cMWSV9tKWBEREZH0GIb0QF5RCXwW/oFihVC17ZvSFa1crCSsioiISDswDNVx287ewqzt51XL/o1tsO2dzjA0kElYFRERkfZgGKqjhBDo/d+jSLyXo2qb2qsZ/tW7uYRVERERaR+GoTooLasAAYsj1dqOzOqBJg3qSVQRERGR9mIYqmMiL6dh3A9n1dqSFvfjtBgREVE5GIbqCCEE3OfsU2ub268FxnfzlKgiIiIi3cAwVAdkFRSj7YI/1NoOz+wBNztOixERET0Lw5COO3r1PkatPa3Wdm1xPxhwWoyIiKhSGIZ0lBAC3T87jJsP81Rti15pjVGBbtIVRUREpIMYhnRQZl4x3vrxrFoQOjHnRThbm0lYFRERkW5iGNIxKw8n4tOI/33TvIXcCBcW9IFMxmkxIiKi6mAY0hFCCISuPI7YWxmqtt0TX4CPq41kNREREdUFDEM64H52ITr8+6Ba27kPg9DAQi5RRURERHUHw5CW++nEdXy0+6JquaObLba83YnTYkRERBrCMKSllEqBTuGRuJddqGr7dlR79G7lKGFVREREdQ/DkBZKSMtG7/8eVWs7OacXnKxNJaqIiIio7mIY0jKrDifhPxFXVMvdm9tj/dgOnBYjIiKqIQxDWkKpFPj093isPpKkatv+TiDau9lKWBUREVHdZyB1AVW1YsUKuLm5wdTUFAEBATh9+nSF/bdt24YWLVrA1NQUbdq0wb59+yrsL4UrqVnwmLtPLQhFfdSbQYiIiKgW6FQY2rJlC6ZPn4758+cjKioKPj4+CA4Oxr1798rsf/z4cYSFhWHcuHGIjo5GaGgoQkNDERcXV8uVl6/FR/sR8tVfquWxL7ghObwfbOuZSFgVERGR/pAJIYTURVRWQEAAOnTogOXLlwMAlEolXF1dMXnyZLz//vul+g8bNgy5ubnYu3evqq1Tp07w9fXF6tWrK/WeWVlZsLa2RmZmJqysrDQzEACZ+cXwWaj+TfMvtXXG8hH+GnsPIiIifVWVz2+dOTNUVFSEc+fOISgoSNVmYGCAoKAgnDhxosxtTpw4odYfAIKDg8vtDwCFhYXIyspSe9WEFz8/rLYcOaM7gxAREZEEdCYMpaenQ6FQwNFR/Tk7jo6OSE1NLXOb1NTUKvUHgPDwcFhbW6terq6uz198GT7o31L17+TwfvC0t6iR9yEiIqKK8W6yp8yZMwfTp09XLWdlZdVIIBrk3wiD/BtpfL9ERERUNToThuzs7GBoaIi0tDS19rS0NDg5OZW5jZOTU5X6A4BcLodczu/8IiIi0hc6M01mYmKCdu3aITIyUtWmVCoRGRmJwMDAMrcJDAxU6w8ABw4cKLc/ERER6R+dOTMEANOnT8fo0aPRvn17dOzYEV999RVyc3MxduxYAMCoUaPQsGFDhIeHAwCmTp2K7t2744svvkD//v2xefNmnD17FmvWrJFyGERERKRFdCoMDRs2DPfv38e8efOQmpoKX19fREREqC6SvnnzJgwM/neyq3Pnzti4cSM+/PBDzJ07F82aNcOuXbvg7e0t1RCIiIhIy+jUc4akUFPPGSIiIqKaUyefM0RERERUExiGiIiISK8xDBEREZFeYxgiIiIivcYwRERERHqNYYiIiIj0GsMQERER6TWGISIiItJrDENERESk13Tq6zik8OQB3VlZWRJXQkRERJX15HO7Ml+0wTD0DNnZ2QAAV1dXiSshIiKiqsrOzoa1tXWFffjdZM+gVCpx9+5dWFpaQiaTPff+srKy4Orqilu3btXJ7zqry+Pj2HRXXR4fx6a76vL4tGFsQghkZ2fDxcVF7Uvcy8IzQ89gYGCARo0aaXy/VlZWde6X/5/q8vg4Nt1Vl8fHsemuujw+qcf2rDNCT/ACaiIiItJrDENERESk1xiGaplcLsf8+fMhl8ulLqVG1OXxcWy6qy6Pj2PTXXV5fLo2Nl5ATURERHqNZ4aIiIhIrzEMERERkV5jGCIiIiK9xjBEREREeo1hqJatWLECbm5uMDU1RUBAAE6fPi11Sc+0YMECyGQytVeLFi1U6wsKCjBx4kQ0aNAAFhYWePXVV5GWlqa2j5s3b6J///4wNzeHg4MDZs2ahZKSktoeCo4ePYoBAwbAxcUFMpkMu3btUlsvhMC8efPg7OwMMzMzBAUFISEhQa3Pw4cPMXLkSFhZWcHGxgbjxo1DTk6OWp/z58+ja9euMDU1haurKz799NOaHtozxzZmzJhSxzEkJEStj7aOLTw8HB06dIClpSUcHBwQGhqK+Ph4tT6a+j08fPgw/P39IZfL0bRpU6xfv17ysfXo0aPUsXvnnXe0fmwAsGrVKrRt21b18L3AwEDs379ftV5Xj1tlxqbLx+1pS5YsgUwmw7Rp01RtunzsShFUazZv3ixMTEzE2rVrxcWLF8Vbb70lbGxsRFpamtSlVWj+/PmidevWIiUlRfW6f/++av0777wjXF1dRWRkpDh79qzo1KmT6Ny5s2p9SUmJ8Pb2FkFBQSI6Olrs27dP2NnZiTlz5tT6WPbt2yc++OADsWPHDgFA7Ny5U239kiVLhLW1tdi1a5eIjY0VL7/8snB3dxf5+fmqPiEhIcLHx0ecPHlS/PXXX6Jp06YiLCxMtT4zM1M4OjqKkSNHiri4OLFp0yZhZmYmvvnmG0nHNnr0aBESEqJ2HB8+fKjWR1vHFhwcLNatWyfi4uJETEyM6Nevn2jcuLHIyclR9dHE7+G1a9eEubm5mD59urh06ZJYtmyZMDQ0FBEREZKOrXv37uKtt95SO3aZmZlaPzYhhNizZ4/47bffxNWrV0V8fLyYO3euMDY2FnFxcUII3T1ulRmbLh+3fzp9+rRwc3MTbdu2FVOnTlW16/KxexrDUC3q2LGjmDhxompZoVAIFxcXER4eLmFVzzZ//nzh4+NT5rqMjAxhbGwstm3bpmq7fPmyACBOnDghhHj8IW1gYCBSU1NVfVatWiWsrKxEYWFhjdZekacDg1KpFE5OTuKzzz5TtWVkZAi5XC42bdokhBDi0qVLAoA4c+aMqs/+/fuFTCYTd+7cEUIIsXLlSlG/fn21sc2ePVt4eXnV8Ij+p7ww9Morr5S7ja6MTQgh7t27JwCII0eOCCE093v43nvvidatW6u917Bhw0RwcHBND0nl6bEJ8fhD9Z8fQk/TlbE9Ub9+ffHdd9/VqeP2xJOxCVE3jlt2drZo1qyZOHDggNp46tqx4zRZLSkqKsK5c+cQFBSkajMwMEBQUBBOnDghYWWVk5CQABcXF3h4eGDkyJG4efMmAODcuXMoLi5WG1eLFi3QuHFj1bhOnDiBNm3awNHRUdUnODgYWVlZuHjxYu0OpALJyclITU1VG4u1tTUCAgLUxmJjY4P27dur+gQFBcHAwACnTp1S9enWrRtMTExUfYKDgxEfH49Hjx7V0mjKdvjwYTg4OMDLywsTJkzAgwcPVOt0aWyZmZkAAFtbWwCa+z08ceKE2j6e9KnNv9Gnx/bEhg0bYGdnB29vb8yZMwd5eXmqdboyNoVCgc2bNyM3NxeBgYF16rg9PbYndP24TZw4Ef379y9VQ106dgC/qLXWpKenQ6FQqP1SAICjoyOuXLkiUVWVExAQgPXr18PLywspKSlYuHAhunbtiri4OKSmpsLExAQ2NjZq2zg6OiI1NRUAkJqaWua4n6zTFk9qKavWf47FwcFBbb2RkRFsbW3V+ri7u5fax5N19evXr5H6nyUkJASDBg2Cu7s7kpKSMHfuXPTt2xcnTpyAoaGhzoxNqVRi2rRpeOGFF+Dt7a16b038HpbXJysrC/n5+TAzM6uJIamUNTYAGDFiBJo0aQIXFxecP38es2fPRnx8PHbs2FFh3U/WVdSnNsZ24cIFBAYGoqCgABYWFti5cydatWqFmJgYnT9u5Y0N0P3jtnnzZkRFReHMmTOl1tWVv7knGIbomfr27av6d9u2bREQEIAmTZpg69attfaLSs9v+PDhqn+3adMGbdu2haenJw4fPoxevXpJWFnVTJw4EXFxcTh27JjUpWhceWMbP3686t9t2rSBs7MzevXqhaSkJHh6etZ2mVXm5eWFmJgYZGZmYvv27Rg9ejSOHDkidVkaUd7YWrVqpdPH7datW5g6dSoOHDgAU1NTqcupcZwmqyV2dnYwNDQsdaV9WloanJycJKqqemxsbNC8eXMkJibCyckJRUVFyMjIUOvzz3E5OTmVOe4n67TFk1oqOkZOTk64d++e2vqSkhI8fPhQ58br4eEBOzs7JCYmAtCNsU2aNAl79+7FoUOH0KhRI1W7pn4Py+tjZWVV48G/vLGVJSAgAADUjp02j83ExARNmzZFu3btEB4eDh8fH3z99dd14riVN7ay6NJxO3fuHO7duwd/f38YGRnByMgIR44cwdKlS2FkZARHR0edP3b/xDBUS0xMTNCuXTtERkaq2pRKJSIjI9Xml3VBTk4OkpKS4OzsjHbt2sHY2FhtXPHx8bh586ZqXIGBgbhw4YLaB+2BAwdgZWWlOp2sDdzd3eHk5KQ2lqysLJw6dUptLBkZGTh37pyqz59//gmlUqn6D11gYCCOHj2K4uJiVZ8DBw7Ay8tLsimysty+fRsPHjyAs7MzAO0emxACkyZNws6dO/Hnn3+WmqrT1O9hYGCg2j6e9KnJv9Fnja0sMTExAKB27LRxbOVRKpUoLCzU6eNWnidjK4suHbdevXrhwoULiImJUb3at2+PkSNHqv5dp45drV6urec2b94s5HK5WL9+vbh06ZIYP368sLGxUbvSXhvNmDFDHD58WCQnJ4u///5bBAUFCTs7O3Hv3j0hxOPbKxs3biz+/PNPcfbsWREYGCgCAwNV2z+5vbJPnz4iJiZGRERECHt7e0lurc/OzhbR0dEiOjpaABBffvmliI6OFjdu3BBCPL613sbGRuzevVucP39evPLKK2XeWu/n5ydOnToljh07Jpo1a6Z2+3lGRoZwdHQUr7/+uoiLixObN28W5ubmNX77eUVjy87OFjNnzhQnTpwQycnJ4uDBg8Lf3180a9ZMFBQUaP3YJkyYIKytrcXhw4fVblPOy8tT9dHE7+GT23xnzZolLl++LFasWFHjt/k+a2yJiYli0aJF4uzZsyI5OVns3r1beHh4iG7dumn92IQQ4v333xdHjhwRycnJ4vz58+L9998XMplM/PHHH0II3T1uzxqbrh+3sjx9d5wuH7unMQzVsmXLlonGjRsLExMT0bFjR3Hy5EmpS3qmYcOGCWdnZ2FiYiIaNmwohg0bJhITE1Xr8/Pzxbvvvivq168vzM3NxcCBA0VKSoraPq5fvy769u0rzMzMhJ2dnZgxY4YoLi6u7aGIQ4cOCQClXqNHjxZCPL69/qOPPhKOjo5CLpeLXr16ifj4eLV9PHjwQISFhQkLCwthZWUlxo4dK7Kzs9X6xMbGii5dugi5XC4aNmwolixZIunY8vLyRJ8+fYS9vb0wNjYWTZo0EW+99VapIK6tYytrXADEunXrVH009Xt46NAh4evrK0xMTISHh4fae0gxtps3b4pu3boJW1tbIZfLRdOmTcWsWbPUnlejrWMTQog33nhDNGnSRJiYmAh7e3vRq1cvVRASQneP27PGpuvHrSxPhyFdPnZPkwkhRO2dhyIiIiLSLrxmiIiIiPQawxARERHpNYYhIiIi0msMQ0RERKTXGIaIiIhIrzEMERERkV5jGCIiIiK9xjBEREREeo1hiIh0khAC48ePh62tLWQymep7n8ry+uuvY/HixRp9//T0dDg4OOD27dsa3S8R1T6GISLSSREREVi/fj327t2LlJQUeHt7l9kvNjYW+/btw5QpU1RtPXr0wLRp00r1Xb9+PWxsbCr1/nZ2dhg1ahTmz59fnfKJSIswDBGRTkpKSoKzszM6d+4MJycnGBkZldlv2bJlGDJkCCwsLDRew9ixY7FhwwY8fPhQ4/smotrDMEREOmfMmDGYPHkybt68CZlMBjc3tzL7KRQKbN++HQMGDKjW+7i5uUEmk5V6PdG6dWu4uLhg586d1do/EWmHsv9XiohIi3399dfw9PTEmjVrcObMGRgaGpbZ7/z588jMzET79u2r9T5nzpyBQqEA8DhYDR48GMbGxmp9OnbsiL/++gvjxo2r1nsQkfQYhohI51hbW8PS0hKGhoZwcnIqt9+NGzdgaGgIBweHUutWrlyJ7777Tq2tpKQEpqamqmV7e3vVv6dOnYqUlBScOXNGbRsXFxdER0dXdyhEpAUYhoiozsrPz4dcLleb2npi5MiR+OCDD9TaduzYUeZdZ2vWrMH333+P48ePqwUkADAzM0NeXp5mCyeiWsUwRER1lp2dHfLy8lBUVAQTExO1ddbW1mjatKlaW1lnkA4dOoTJkydj06ZNaNu2ban1Dx8+LBWQiEi38AJqIqqzfH19AQCXLl2q1vaJiYkYPHgw5s6di0GDBpXZJy4uDn5+ftUtkYi0AMMQEdVZ9vb28Pf3x7Fjx6q8bX5+PgYMGAA/Pz+MHz8eqampqtcTeXl5OHfuHPr06aPJsomoljEMEVGd9uabb2LDhg1V3i4tLQ1XrlxBZGQkXFxc4OzsrHo9sXv3bjRu3Bhdu3bVZMlEVMtkQgghdRFERDUlPz8fXl5e2LJlCwIDAzW6706dOmHKlCkYMWKERvdLRLWLZ4aIqE4zMzPDjz/+iPT0dI3uNz09HYMGDUJYWJhG90tEtY9nhoiIiEiv8cwQERER6TWGISIiItJrDENERESk1xiGiIiISK8xDBEREZFeYxgiIiIivcYwRERERHqNYYiIiIj0GsMQERER6bX/A2K5cm1nFyQzAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -876,7 +967,7 @@ "plt.plot(f, (phaseLAL.real - (psi_bbh + phase_shift)))\n", "# plt.xscale('log')\n", "# plt.yscale('log')\n", - "plt.title(r'NRTidalv3-aligned $\\Delta \\psi_{BBH}^{XAS}$')\n", + "plt.title(r'NRTidalv3-aligned $\\Delta (\\psi_{XAS} + \\delta \\psi_{T})$')\n", "plt.ylabel(r'$\\Delta \\psi$')\n", "plt.xlabel('f (Hz)')\n", "plt.show()" @@ -895,42 +986,49 @@ "id": "8cf262e7", "metadata": {}, "source": [ - "Subtract XAS (non-aligned) baseline from LAL XAS-NRTidalv3-aligned to get phase offsets" + "Currently, the phase that we get from **LAL** consists of the non-algned XAS BBH baseline + the tidal alignment:\n", + "$$\\psi^{LAL} = \\psi_{XAS}(f) - [\\psi_{XAS}(f_{ref}) - \\psi_T(f_{ref}) - d\\psi_{XAS}(f_{ref}) M_s f_{ref} + d\\psi_T(f_{ref}) M_s f_{ref}] + 2\\psi_0 + \\frac{\\pi}{4} + b M_s f$$\n", + "where $M_s$ is the total mass of the system in seconds. *If I am not mistaken*, b should be given by: $-(d\\psi_{XAS}(f_{ref}) - d\\psi_T(f_{ref}))$ (see lines 737-739 of LALSimIMRPhenomX.c)\n", + "\n", + "Considering the ripple and LAL XAS baselines agree well, we can use the non-phase-aligned ripple XAS phase to remove this baseline and directly compare the ripple and LAL tidal alignment corrections." ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 28, "id": "e6521469", "metadata": {}, "outputs": [], "source": [ "from ripplegw.waveforms.IMRPhenomXAS import Phase\n", "from ripplegw.waveforms.IMRPhenomX_utils import PhenomX_phase_coeff_table as bbh_phase_coeffs\n", + "from ripplegw.waveforms import IMRPhenomX_utils\n", "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import fullTidalPhaseCorrection" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 29, "id": "d0a925a3", "metadata": {}, "outputs": [], "source": [ "ripple_XAS_phase = Phase(f, jnp.array([m1,m2,chi1,chi2]), bbh_phase_coeffs)\n", - "LAL_phase_offset = phaseLAL.real - ripple_XAS_phase # Should consist of linb*Mf + lina(=0) + phifRef\n", - "LAL_linbMf = LAL_phase_offset + phifRef # will probably contain a slight error due to dphi_merger*fref" + "LAL_phase_offset = phaseLAL.real - ripple_XAS_phase # Should consist of linb*Mf + lina(=0) + phifRef (LAL!!)\n", + "\n", + "# b * M * (f - f_ref) = \\Delta\\psi + phifRef(ripple) - phiTfRef\n", + "bMf_min_fref = LAL_phase_offset + phifRef - phiTfRef # phiTfRef is defined with opposite sign from LAL" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 30, "id": "586a19ba", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -940,106 +1038,26 @@ } ], "source": [ - "plt.plot(f, LAL_linbMf)\n", - "plt.plot(f, -dphi_merger*(f*M_s))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3cf626db", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "89afcebc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(f, (phaseLAL.real - (psi_bbh + phase_shift)) - LAL_linbMf) # difference appears to be linbMf?\n", - "plt.yscale('log')\n", + "plt.plot(f, bMf_min_fref, label=r'$M_s(f-f_{ref})\\text{linb}$')\n", + "plt.plot(f, dphi_merger*M_s*(f - f_ref), ls='--', label=r'$M_s(f-f_{ref})d\\psi_{merger}$')\n", + "plt.legend()\n", + "# plt.yscale('log')\n", + "# plt.xscale('log')\n", "plt.show()" ] }, { - "cell_type": "code", - "execution_count": 39, - "id": "e95eb1f8", - "metadata": {}, - "outputs": [], - "source": [ - "dphiXAS = jax.grad(Phase)(f_merger, theta_intrinsic[:4], bbh_phase_coeffs)\n", - "dphiNRTidal = jax.grad(fullTidalPhaseCorrection)(f_merger, theta_intrinsic, f_merger, no_taper=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d7c866b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0020110738\n", - "-0.036770605\n", - "-0.03878169\n", - "[3.9062500e-03 2.9296875e-03 1.9531250e-03 ... 1.6522461e+01 1.6522461e+01\n", - " 1.6522461e+01]\n" - ] - } - ], - "source": [ - "print(dphiXAS)\n", - "print(dphiNRTidal)\n", - "print(dphi_merger)\n", - "print(LAL_linbMf)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "0db64257", + "cell_type": "markdown", + "id": "9ab61483", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "plt.plot(f, fullTidalPhaseCorrection(f, theta_intrinsic, f_merger, no_taper=False))\n", - "plt.plot(f, dphiNRTidal*(f-f_merger) + fullTidalPhaseCorrection(f_merger, theta_intrinsic, f_merger, no_taper=False))\n", - "plt.scatter(f_merger, fullTidalPhaseCorrection(f_merger, theta_intrinsic, f_merger, no_taper=False))\n", - "plt.show()" + "This is good enough for now" ] }, { "cell_type": "code", "execution_count": null, - "id": "99a3e0eb", + "id": "6923a6d2", "metadata": {}, "outputs": [], "source": [] diff --git a/test/benchmark_waveform.py b/test/benchmark_waveform.py index ccfcb105..e2833157 100644 --- a/test/benchmark_waveform.py +++ b/test/benchmark_waveform.py @@ -12,8 +12,8 @@ import pandas as pd from tqdm import tqdm -from ripple import get_eff_pads, get_match_arr, ms_to_Mc_eta, lambdas_to_lambda_tildes -from ripple.constants import PI +from ripplegw import get_eff_pads, get_match_arr, ms_to_Mc_eta, lambdas_to_lambda_tildes +from ripplegw.constants import PI import lal import lalsimulation as lalsim @@ -26,8 +26,8 @@ def check_is_tidal(waveform_name: str): # Check if the given waveform is supported: - bns_waveforms = ["IMRPhenomD_NRTidalv2", "TaylorF2"] - bbh_waveforms = ["IMRPhenomD"] + bns_waveforms = ["IMRPhenomD_NRTidalv2", "TaylorF2", "IMRPhenomXAS_NRTidalv3"] + bbh_waveforms = ["IMRPhenomD", "IMRPhenomXAS"] all_waveforms = bns_waveforms + bbh_waveforms if waveform_name not in all_waveforms: @@ -43,7 +43,7 @@ def check_is_tidal(waveform_name: str): def get_jitted_waveform(waveform_name: str, fs: np.array, f_ref: float): if waveform_name == "IMRPhenomD": # Import the waveform - from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_hphc as waveform_generator + from ripplegw.waveforms.IMRPhenomD import gen_IMRPhenomD_hphc as waveform_generator # Get jitted version (note, use IMRPhenomD as underlying waveform model) @jax.jit @@ -53,7 +53,7 @@ def waveform(theta): elif waveform_name == "IMRPhenomD_NRTidalv2": # Import the waveform - from ripple.waveforms.X_NRTidalv2 import gen_NRTidalv2_hphc as waveform_generator + from ripplegw.waveforms.X_NRTidalv2 import gen_NRTidalv2_hphc as waveform_generator # Get jitted version (note, use IMRPhenomD as underlying waveform model) @jax.jit @@ -61,9 +61,29 @@ def waveform(theta): hp, _ = waveform_generator(fs, theta, f_ref, IMRphenom="IMRPhenomD") return hp + elif waveform_name == "IMRPhenomXAS": + # Import the waveform + from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc as waveform_generator + + # Get jitted version (note, use IMRPhenomD as underlying waveform model) + @jax.jit + def waveform(theta): + hp, _ = waveform_generator(fs, theta, f_ref) + return hp + + elif waveform_name == "IMRPhenomXAS_NRTidalv3": + # Import the waveform + from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc as waveform_generator + + # Get jitted version (note, use IMRPhenomD as underlying waveform model) + @jax.jit + def waveform(theta): + hp, _ = waveform_generator(fs, theta, f_ref) + return hp + elif waveform_name == "TaylorF2": # Import the waveform - from ripple.waveforms.TaylorF2 import gen_TaylorF2_hphc as waveform_generator + from ripplegw.waveforms.TaylorF2 import gen_TaylorF2_hphc as waveform_generator # Get jitted version @jax.jit @@ -123,6 +143,9 @@ def random_match(n: int, bounds: dict, IMRphenom: str = "IMRPhenomD_NRTidalv2", # Get a frequency domain waveform thetas = [] matches = [] + + import os + psd_file = os.path.join(os.path.dirname(__file__), psd_file) f_ASD, ASD = np.loadtxt(psd_file, unpack=True) ASD = np.sqrt(ASD) @@ -543,7 +566,7 @@ def lal_waveform_no_tidal(theta): "lambda": [0.0, 5000.0], "d_L": [30.0, 300.0]} - approximant = "TaylorF2" + approximant = "IMRPhenomXAS_NRTidalv3" print(f"Checking approximant {approximant}") print("Checking mismatches wrt LAL") df = random_match(1000, bounds, approximant, outdir = "./") From 3b98a875db813c1898f8f6b3152128b5792611b2 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Fri, 7 Nov 2025 14:30:51 +0100 Subject: [PATCH 030/474] Cleaned up code and updated benchmark --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 123 ++++--------- src/ripplegw/waveforms/NRTidalv3_utils.py | 168 +++++++----------- test/benchmark_waveform.py | 2 +- 3 files changed, 91 insertions(+), 202 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index 1cb77205..d16d236b 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -2,39 +2,14 @@ import jax import jax.numpy as jnp -from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C +from ..constants import gt, PI from ..typing import Array from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas from .IMRPhenom_tidal_utils import get_kappa from .IMRPhenomD_NRTidalv2 import get_spin_phase_correction, get_planck_taper, get_tidal_amplitude # Same between v2 and v3 -from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper +from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper, fullTidalPhaseCorrection from ripplegw.waveforms import IMRPhenomX_utils from .IMRPhenomXAS import Amp, Phase -import lalsimulation as lalsim -import lal - - -def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, f_final: float, no_taper: bool): - # Decide whether to include the Planck taper or not - if no_taper: - P_P = jnp.ones_like(f) - else: - P_P = general_planck_taper(f, 1.15*f_final, 1.35*f_final) - - m1, m2, _, _, lambda1, lambda2 = theta_intrinsic - M_s = (m1 + m2) * gt - Xa = m1 / (m1 + m2) - x = PI * f * M_s - x_23 = x**(2.0/3.0) - - PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) - NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) - NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) - - psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P - psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) - - return psi_T + psi_SS # This could be a general utils function @@ -46,7 +21,6 @@ def _gen_IMRPhenomXAS_NRTidalv3( bbh_amp: Array, bbh_psi: Array, no_taper: bool = False, - get_phase: bool = False ): """ Master internal function to get the GW strain for given parameters. The function takes @@ -85,23 +59,34 @@ def _gen_IMRPhenomXAS_NRTidalv3( f_final ) + if no_taper: + P_P = jnp.ones_like(f) + P_P_fref = 1.0 + P_P_ffinal = 1.0 + A_P = jnp.ones_like(f) + else: + P_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) + P_P_fref = general_planck_taper(f_ref, 1.15*f_merger, 1.35*f_merger) + P_P_ffinal = general_planck_taper(f_final, 1.15*f_merger, 1.35*f_merger) + A_P = get_planck_taper(f, f_merger) + bbh_phase_coeffs = IMRPhenomX_utils.PhenomX_phase_coeff_table # Note: the π shift from Y22 has been moved to the calculation of h0 phifRef = ( Phase(f_ref, theta_intrinsic[:4], bbh_phase_coeffs) - PI / 4.0 ) # This is part of the BBH phase alignment - phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, f_merger, no_taper) # This is part of the tidal correction to the phase alignment + phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, P_P_fref) # This is part of the tidal correction to the phase alignment dphiXAS = jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs) - dphiT = jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, f_merger, no_taper) + dphiT = jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, P_P_ffinal) dphi_merger = -(dphiXAS - dphiT) / M_s # linb from LAL ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] phase_shift = -(phifRef - phiTfRef + dphi_merger*(f_ref*M_s)) + dphi_merger*f_Ms + ext_phase_contrib - # Get tidal phase and spin corrections for BNS + # Get tidal phase and spin corrections for BNS PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) @@ -116,22 +101,10 @@ def _gen_IMRPhenomXAS_NRTidalv3( # mask = (jnp.arange(f.size) >= idx) # NRTidalv3_phase = jnp.where(mask, tidal_min_value, NRTidalv3_phase) - - # Redefine planck taper as LAL uses the merger frequency for this computation, not f_final (which can be f_merger, but isn't guaranteed) - # Ok, this^ is not correct, planck taper can be passed as argument to fullTidalPhaseCorrection - if no_taper: - P_P = jnp.ones_like(f) - A_P = jnp.ones_like(f) - else: - P_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) - A_P = get_planck_taper(f, f_merger) psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) - if get_phase: # purely for debugging purposes - return bbh_psi, phase_shift, phifRef, phiTfRef, dphiXAS, dphiT, dphi_merger, psi_T, psi_SS - # Reconstruct waveform with NRTidal terms included: h(f) = [A(f) + A_tidal(f)] * Exp{I [phi(f) - phi_tidal(f)]} * window(f) h0 = A_P * (bbh_amp + A_T) * jnp.exp(1.0j * ((bbh_psi + phase_shift + PI) - (psi_T + psi_SS))) # The additional π shift comes from Y22 @@ -144,20 +117,20 @@ def gen_IMRPhenomXAS_NRTidalv3( f_ref: float, use_lambda_tildes: bool = True, no_taper: bool = False, - get_phase: bool = False ) -> Array: """ - Generate NRTidalv3 frequency domain waveform following NRTidalv3 paper. - vars array contains both intrinsic and extrinsic variables - theta = [Mchirp, eta, chi1, chi2, D, tc, phic] - Mchirp: Chirp mass of the system [solar masses] - eta: Symmetric mass ratio [between 0.0 and 0.25] - chi1: Dimensionless aligned spin of the primary object [between -1 and 1] - chi2: Dimensionless aligned spin of the secondary object [between -1 and 1] - lambda1: Dimensionless tidal deformability of primary object - lambda2: Dimensionless tidal deformability of secondary object - D: Luminosity distance to source [Mpc] - tc: Time of coalesence. This only appears as an overall linear in f contribution to the phase + Generate NRTidalv3 frequency domain waveform following 2311.07456. + + params array contains both intrinsic and extrinsic variables + theta = [Mchirp, eta, chi1, chi2, D, tc, phic]: + Mchirp: Chirp mass of the system [solar masses] + eta: Symmetric mass ratio [between 0.0 and 0.25] + chi1: Dimensionless aligned spin of the primary object [between -1 and 1] + chi2: Dimensionless aligned spin of the secondary object [between -1 and 1] + lambda1: Dimensionless tidal deformability of primary object + lambda2: Dimensionless tidal deformability of secondary object + D: Luminosity distance to source [Mpc] + tc: Time of coalesence. This only appears as an overall linear in f contribution to the phase phic: Phase of coalesence f_ref: Reference frequency for the waveform @@ -184,43 +157,7 @@ def gen_IMRPhenomXAS_NRTidalv3( # Generate the BBH part: bbh_theta_intrinsic = jnp.array([m1, m2, chi1, chi2]) - # m1_s = m1 * gt - # m2_s = m2 * gt - - # M_s = m1_s + m2_s - # eta = m1_s * m2_s / (M_s**2.0) - # delta = jnp.sqrt(1.0 - 4.0 * eta) - # mm1 = 0.5 * (1.0 + delta) - # mm2 = 0.5 * (1.0 - delta) - - # StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) - # chia = chi1 - chi2 - - # fM_s = f * M_s - # fMs_RD, fMs_damp, _, _ = IMRPhenomX_utils.get_cutoff_fMs(m1, m2, chi1, chi2) Psi = Phase(f, bbh_theta_intrinsic, phase_coeffs) - - # linb is cancelled by itself in 737-738 of LALSimIMRPhenomX.c and replaced by its tidal equivalent - # For ease of computation and structure, the phase alignment terms are added in _gen_IMRPhenomXAS_NRTidalv3 (even the non-tidal ones as the tidal merger frequency is different from the BBH merger frequency) - - # Generate the linear in f and constant contribution to the phase in order - # to roll the waveform such that the peak is at the input tc and phic - # lina, linb, psi4tostrain = IMRPhenomX_utils.calc_phaseatpeak( - # eta, StotR, chia, delta - # ) - # dphi22Ref = ( - # jax.grad(Phase)((fMs_RD - fMs_damp) / M_s, bbh_theta_intrinsic, phase_coeffs) / M_s - # ) - # linb = linb - dphi22Ref - 2.0 * PI * (500.0 + psi4tostrain) - # The additional π shift comes from Y22 - # phifRef = ( - # -(Phase(f_ref, theta_intrinsic[:4], phase_coeffs)) - # + PI / 4.0 - # + PI - # ) - # ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] - # Psi = Psi + phifRef - 2 * PI + ext_phase_contrib - A = Amp(f, bbh_theta_intrinsic, amp_coeffs, D=theta_extrinsic[0]) bbh_amp = A @@ -228,7 +165,7 @@ def gen_IMRPhenomXAS_NRTidalv3( # Use BBH waveform and add tidal corrections return _gen_IMRPhenomXAS_NRTidalv3( - f, f_ref, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper, get_phase=get_phase + f, f_ref, theta_intrinsic, theta_extrinsic, bbh_amp, bbh_psi, no_taper=no_taper ) @@ -242,7 +179,7 @@ def gen_IMRPhenomXAS_NRTidalv3_hphc( """ vars array contains both intrinsic and extrinsic variables - IMRphenom denotes the name of the underlying BBH approximant used, before applying tidal corrections. + IMRPhenom denotes the name of the underlying BBH approximant used, before applying tidal corrections. theta = [Mchirp, eta, chi1, chi2, lambda1, lambda2, D, tc, phic, inclination] Mchirp: Chirp mass of the system [solar masses] diff --git a/src/ripplegw/waveforms/NRTidalv3_utils.py b/src/ripplegw/waveforms/NRTidalv3_utils.py index 22453f85..b237b96b 100644 --- a/src/ripplegw/waveforms/NRTidalv3_utils.py +++ b/src/ripplegw/waveforms/NRTidalv3_utils.py @@ -2,11 +2,10 @@ import jax import jax.numpy as jnp -from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C +from ..constants import gt, PI, TWO_PI from ..typing import Array -from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas -from .IMRPhenom_tidal_utils import get_quadparam_octparam, get_kappa -from .IMRPhenomD_NRTidalv2 import get_tidal_amplitude +from .IMRPhenom_tidal_utils import get_kappa +from .IMRPhenomD_NRTidalv2 import get_spin_phase_correction """ @@ -34,7 +33,6 @@ def _get_merger_frequency(theta: Array): Args: theta (Array): Intrinsic parameters with order (m1, m2, chi1, chi2, lambda1, lambda2) - kappa (float, optional): Tidal parameter kappa. Defaults to None, so that it is computed from the given parameters theta. Returns: float: The merger frequency in Hz. @@ -99,11 +97,41 @@ def _get_merger_frequency(theta: Array): # convert from angular frequency to frequency (divide by 2*pi) and then convert from dimensionless frequency to Hz (divide by mtot * LAL_MTSUN_SI) - fHz_merger = Momega_merger / TWO_PI / (M * MRSUN / C) + fHz_merger = Momega_merger / TWO_PI / (M * gt) return fHz_merger +# Full tidal correction +def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, P_P: Array): + """ + Returns the NRTidalv3 phase corrections due to tidal and spin effects. + + Args: + f (Array):Frequency array + theta_intrinsic (Array): Intrinsic parameters of the system [m1, m2, chi1, chi2, lambda1, lambda2] + P_P (Array): Taper function. If no_taper was set to True in the master function, then this is just an array of ones. Otherwise this is the Planck taper + + Returns: + Array: The NRTidalv3 phase corrections due to tidal and spin effects + """ + + m1, m2, _, _, lambda1, lambda2 = theta_intrinsic + M_s = (m1 + m2) * gt + Xa = m1 / (m1 + m2) + x = PI * f * M_s + x_23 = x**(2.0/3.0) + + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) + + psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P + psi_SS = get_spin_phase_correction(x_23, theta_intrinsic) + + return psi_T + psi_SS + + ##################################################################################################################################### ################################################### TIDAL PHASE CORRECTIONS ################################################### ##################################################################################################################################### @@ -121,17 +149,22 @@ def general_planck_taper(x, y1, y2): ) -""" -Tidal phase correction for NRTidalv3, Eq. (27,30), from Abac, et. al. (2023) (https://arxiv.org/pdf/2311.07456.pdf) -and is a function of x = angular_orb_freq^(2./3.) -""" def get_tidal_phase( - M_omega: Array, # Dimensionless angular GW frequency - NRTidalv3_coeffs: Array, # NRTidalv3 coefficients - PN_coeffs: Array # 7.5 PN coefficients to be used as constraints + M_omega: Array, + NRTidalv3_coeffs: Array, + PN_coeffs: Array ): """ - SimNRTunedTidesFDTidalPhase_v3 + Tidal phase correction for NRTidalv3, Eq. (27,30), from Abac, et. al. (2023) (https://arxiv.org/pdf/2311.07456.pdf) + Jaxified version of SimNRTunedTidesFDTidalPhase_v3. + + Args: + M_omega (Array): Dimensionless angular GW frequency + NRTidalv3_coeffs (Array): NRTidalv3 coefficients + PN_coeffs (Array): 7.5 PN coefficients to be used as constraints + + Returns: + tidal_phase (Array): Corrections to the phase due to (dynamical) tides """ s1 = NRTidalv3_coeffs[0] @@ -202,11 +235,6 @@ def get_tidal_phase( return tidal_phase -""" -PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, -see Eq. (22) and (45) of https://arxiv.org/pdf/2311.07456.pdf -and is a function of x = angular_orb_freq^(2./3.) -""" def get_tidal_phase_PN( M_omega, # Dimensionless angular GW frequency Xa, #< Mass of companion 1 divided by total mass @@ -215,8 +243,20 @@ def get_tidal_phase_PN( PN_coeffs #< 7.5 PN coefficients ): """ - SimNRTunedTidesFDTidalPhase_PN + PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger, see Eq. (22) and (45) of https://arxiv.org/pdf/2311.07456.pdf + Jaxified version of SimNRTunedTidesFDTidalPhase_PN + + Args: + M_omega (Array): Dimensionless angular GW frequency + Xa (float): Mass of companion 1 divided by total mass + lambda1 (float): Dimensionless tidal deformability of companion 1 + lambda2 (float): Dimensionless tidal deformability of companion 2 + PN_coeffs (Array): 7.5 PN coefficients + + Returns: + tidal_phasePN (Array): PN tidal phase correction, at 7.5PN, to connect with NRTidalv3 Phase post-merger """ + Xb = 1.0 - Xa PN_x = M_omega **(2.0/3.0) # pow(M_omega, 2.0/3.0) @@ -390,94 +430,6 @@ def get_NRTidalv3_coefficients( NRTidalv3_coeffs = NRTidalv3_coeffs.at[19].set(-(c_5over2B + c_3over2B*NRTidalv3_coeffs[11] - NRTidalv3_coeffs[9]) * inv_c1_B) return NRTidalv3_coeffs - - -###################################################################################################################################### -######################################################### SPIN EFFECTS ######################################################### -###################################################################################################################################### - - -def get_spin_phase_correction_v3(x: Array, theta: Array) -> Array: - """ - Get the higher order spin corrections with updated unversal relation, as detailed in Section III A of 2311.07456 - - Args: - x (Array): Angular frequency, in particular, x = (pi M f)^(2/3) - theta (Array): Intrinsic parameters (mass1, mass2, chi1, chi2, lambda1, lambda2) - - Returns: - Array: Higher order spin corrections to the phase - """ - - # Compute auxiliary quantities - m1, m2, chi1, chi2, lambda1, lambda2 = theta - m1_s = m1 * gt - m2_s = m2 * gt - M_s = m1_s + m2_s - eta = m1_s * m2_s / (M_s**2.0) - - # Compute the auxiliary variables - X1 = m1_s / M_s - X1sq = X1 * X1 - chi1_sq = chi1 * chi1 - - X2 = m2_s / M_s - X2sq = X2 * X2 - chi2_sq = chi2 * chi2 - - # Compute quadrupole parameters - quadparam1, octparam1 = get_quadparam_octparam(lambda1) - quadparam2, octparam2 = get_quadparam_octparam(lambda2) - - # Remove 1 for the BBH baseline, from here on, quadparam is "quadparam hat" as referred to in the NRTidalv2 paper etc - quadparam1 -= 1 - quadparam2 -= 1 - octparam1 -= 1 - octparam2 -= 1 - - # Get phase contributions - SS_2 = -50.0 * quadparam1 * chi1_sq * X1sq - 50.0 * quadparam2 * chi2_sq * X2sq - - SS_3 = ( - 5.0 - / 84.0 - * (9407.0 + 8218.0 * X1 - 2016.0 * X1sq) - * quadparam1 - * X1sq - * chi1_sq - + 5.0 - / 84.0 - * (9407.0 + 8218.0 * X2 - 2016.0 * X2sq) - * quadparam2 - * X2sq - * chi2_sq - ) - - SS_3p5 = ( - -400.0 * PI * quadparam1 * chi1_sq * X1sq - - 400.0 * PI * quadparam2 * chi2_sq * X2sq - ) - SSS_3p5 = ( - 10.0 - * ((X1sq + 308.0 / 3.0 * X1) * chi1 + (X2sq - 89.0 / 3.0 * X2) * chi2) - * quadparam1 - * X1sq - * chi1_sq - + 10.0 - * ((X2sq + 308.0 / 3.0 * X2) * chi2 + (X1sq - 89.0 / 3.0 * X1) * chi1) - * quadparam2 - * X2sq - * chi2_sq - - 440.0 * octparam1 * X1 * X1sq * chi1_sq * chi1 - - 440.0 * octparam2 * X2 * X2sq * chi2_sq * chi2 - ) - - prefac = 3.0 / (128.0 * eta) - psi_SS = prefac * ( - SS_2 * x ** (-1.0 / 2.0) + SS_3 * x ** (1.0 / 2.0) + (SS_3p5 + SSS_3p5) * x - ) - - return psi_SS ####################################################################################################################################### diff --git a/test/benchmark_waveform.py b/test/benchmark_waveform.py index e2833157..17481b4b 100644 --- a/test/benchmark_waveform.py +++ b/test/benchmark_waveform.py @@ -159,7 +159,7 @@ def random_match(n: int, bounds: dict, IMRphenom: str = "IMRPhenomD_NRTidalv2", thetas = np.array(thetas) matches = np.array(matches) if outdir is not None: - csv_name = f"{outdir}/matches_{IMRphenom}.csv" + csv_name = f"{outdir}/matches_{IMRphenom}_2.csv" print(f"Saving matches to {csv_name}") df = save_matches(csv_name, thetas, matches, is_tidal=is_tidal, verbose=True) From cac4e4ff46a39110ea7068c1414a72ee0756422a Mon Sep 17 00:00:00 2001 From: robkamcha Date: Mon, 10 Nov 2025 11:47:19 +0100 Subject: [PATCH 031/474] Change to tidal phase upon minimum added --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 25 +- src/ripplegw/waveforms/NRTidalv3_utils.py | 7 + test/NRTidalv3_systematic_test.ipynb | 412 ------- test/NRTidalv3_testing.ipynb | 1087 ----------------- test/benchmark_waveform.py | 2 +- 5 files changed, 22 insertions(+), 1511 deletions(-) delete mode 100644 test/NRTidalv3_systematic_test.ipynb delete mode 100644 test/NRTidalv3_testing.ipynb diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index d16d236b..e9f9d40c 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -7,12 +7,11 @@ from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas from .IMRPhenom_tidal_utils import get_kappa from .IMRPhenomD_NRTidalv2 import get_spin_phase_correction, get_planck_taper, get_tidal_amplitude # Same between v2 and v3 -from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper, fullTidalPhaseCorrection +from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper, fullTidalPhaseCorrection, changePhase_if_min from ripplegw.waveforms import IMRPhenomX_utils from .IMRPhenomXAS import Amp, Phase -# This could be a general utils function def _gen_IMRPhenomXAS_NRTidalv3( f: Array, f_ref: float, @@ -91,15 +90,19 @@ def _gen_IMRPhenomXAS_NRTidalv3( NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) - # # TODO: Check for local minimum -> this doesn't seem to work correctly at the moment - # fHzmrgcheck = 0.9 * f_merger - # increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) - # valid = (f >= fHzmrgcheck) & increasing - # if jnp.any(valid): # if local minimum found: set NRTidalv3 phase to this vale afterwards - # idx = jnp.argmax(valid) - # tidal_min_value = NRTidalv3_phase[idx] - # mask = (jnp.arange(f.size) >= idx) - # NRTidalv3_phase = jnp.where(mask, tidal_min_value, NRTidalv3_phase) + # TODO: Check for local minimum -> this doesn't seem to work correctly at the moment + fHzmrgcheck = 0.9 * f_merger + increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) + valid = (f >= fHzmrgcheck) & increasing + + # if local minimum found: set NRTidalv3 phase to this value afterwards + x_lax = (f, NRTidalv3_phase, valid) + NRTidalv3_phase = jax.lax.cond( + jnp.any(valid), + lambda arr: changePhase_if_min(*arr), + lambda arr: arr[1], + x_lax + ) psi_T = NRTidalv3_phase * (1 - P_P) + get_tidal_phase_PN(x, Xa, lambda1, lambda2, PN_coeffs) * P_P diff --git a/src/ripplegw/waveforms/NRTidalv3_utils.py b/src/ripplegw/waveforms/NRTidalv3_utils.py index b237b96b..3745be28 100644 --- a/src/ripplegw/waveforms/NRTidalv3_utils.py +++ b/src/ripplegw/waveforms/NRTidalv3_utils.py @@ -132,6 +132,13 @@ def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, P_P: Array): return psi_T + psi_SS +def changePhase_if_min(f, NRTidalv3_phase, valid): + idx = jnp.argmax(valid) + tidal_min_value = NRTidalv3_phase[idx] + mask = (jnp.arange(f.size) >= idx) + return jnp.where(mask, tidal_min_value, NRTidalv3_phase) + + ##################################################################################################################################### ################################################### TIDAL PHASE CORRECTIONS ################################################### ##################################################################################################################################### diff --git a/test/NRTidalv3_systematic_test.ipynb b/test/NRTidalv3_systematic_test.ipynb deleted file mode 100644 index a2486086..00000000 --- a/test/NRTidalv3_systematic_test.ipynb +++ /dev/null @@ -1,412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 111, - "id": "5eee29c4", - "metadata": {}, - "outputs": [], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "from ripplegw.constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C\n", - "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc, gen_IMRPhenomXAS_NRTidalv3\n", - "from ripplegw import ms_to_Mc_eta, lambdas_to_lambda_tildes, get_eff_pads, get_match_arr\n", - "from ripplegw.waveforms.NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper\n", - "from ripplegw.waveforms.IMRPhenomD_NRTidalv2 import get_planck_taper, get_tidal_amplitude, get_spin_phase_correction\n", - "from ripplegw.waveforms.IMRPhenomXAS import Phase, gen_IMRPhenomXAS\n", - "from ripplegw.waveforms.IMRPhenom_tidal_utils import get_kappa\n", - "\n", - "import lalsimulation as lalsim\n", - "import lal\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "d1a9f328", - "metadata": {}, - "outputs": [], - "source": [ - "# Frequency grid\n", - "T = 128\n", - "f_l = 20.0\n", - "f_sampling = 2 * 4096\n", - "f_u = f_sampling // 2\n", - "f_ref = f_l\n", - "\n", - "delta_t = 1 / f_sampling\n", - "tlen = int(round(T / delta_t))\n", - "freqs = np.fft.rfftfreq(tlen, delta_t)\n", - "df = freqs[1] - freqs[0]\n", - "fs = freqs[(freqs > f_l) & (freqs < f_u)]" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "9165e1ae", - "metadata": {}, - "outputs": [], - "source": [ - "m1 = 1.37 # In solar masses\n", - "m2 = 1.37\n", - "chi1 = 0.141 # Dimensionless spin\n", - "chi2 = 0.141\n", - "lambda1 = 1001.8\n", - "lambda2 = 1001.8\n", - "tc = 0.0 # Time of coalescence in seconds\n", - "phic = 0.0 # Time of coalescence\n", - "dist_mpc = 440 # Distance to source in Mpc\n", - "inclination = 0.0 # Inclination Angle\n", - "\n", - "q = m1/m2\n", - "Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", - "lambda_tilde, delta_lambda_tilde = lambdas_to_lambda_tildes(\n", - " jnp.array([lambda1, lambda2, m1, m2])\n", - ")\n", - "\n", - "theta_ripple = jnp.array(\n", - " [\n", - " Mc,\n", - " eta,\n", - " chi1,\n", - " chi2,\n", - " dist_mpc,\n", - " tc,\n", - " phic,\n", - " inclination,\n", - " ]\n", - ")\n", - "fs_ripple = jnp.arange(f_l, f_u, df)[1:]" - ] - }, - { - "cell_type": "markdown", - "id": "5e9166a4", - "metadata": {}, - "source": [ - "### BBH baseline check" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "85d5ced5", - "metadata": {}, - "outputs": [], - "source": [ - "# from ripplegw.waveforms import IMRPhenomX_utils\n", - "XAS_angle_ripple, lina, linb, fM_s, phifRef, psi4tostrain = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref, get_phase=True)\n", - "# XAS_angle_ripple = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "598640a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n", - "305.656\n", - "[0.00027002 0.00027013 0.00027023 ... 0.05527867 0.05527878 0.05527889]\n", - "12529.15\n", - "9.4438925\n" - ] - } - ], - "source": [ - "print(lina)\n", - "print(linb)\n", - "print(fM_s)\n", - "print(phifRef)\n", - "print(psi4tostrain)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "bc4facc1", - "metadata": {}, - "outputs": [], - "source": [ - "theta = np.array(\n", - " [m1, m2, chi1, chi2, dist_mpc, tc, phic, inclination]\n", - ")\n", - "\n", - "m1_kg = theta[0] * lal.MSUN_SI\n", - "m2_kg = theta[1] * lal.MSUN_SI\n", - "distance = dist_mpc * 1e6 * lal.PC_SI\n", - "\n", - "laldict_OnlyPhase = lal.CreateDict()\n", - "\n", - "# Specify we only want the phase contribution. This exits the function before any NRTidal contributions so don't use this to test tidal phase contributions!\n", - "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", - "\n", - "LAL_OnlyPhase = lalsim.SimIMRPhenomXASGenerateFD(\n", - " m1_kg,\n", - " m2_kg,\n", - " chi1,\n", - " chi2,\n", - " distance,\n", - " f_l,\n", - " f_u,\n", - " df,\n", - " phic,\n", - " f_ref,\n", - " laldict_OnlyPhase,\n", - ")\n", - "\n", - "freqs_lal = np.arange(len(LAL_OnlyPhase.data.data)) * df\n", - "\n", - "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", - "XAS_angle_LAL = LAL_OnlyPhase.data.data[mask_lal]\n", - "f = freqs_lal[mask_lal]" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "8d8d22bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3.1413274+0j)\n" - ] - } - ], - "source": [ - "offset = XAS_angle_LAL[0] - XAS_angle_ripple[0]\n", - "print(offset)" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "bbcbfb62", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 5))\n", - "plt.plot(f, abs((XAS_angle_LAL - (XAS_angle_ripple))), \"-\", color=\"black\")\n", - "name = \"Angle\"\n", - "# plt.legend()\n", - "plt.title(\n", - " r\"{} $h22$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", - " name, m1, m2, chi1, chi2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(f\"{name}\")\n", - "plt.xscale(\"log\")\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "5c0b494e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/matplotlib/cbook.py:1719: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/matplotlib/cbook.py:1355: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 5))\n", - "plt.plot(f, XAS_angle_LAL, label='LAL')\n", - "plt.plot(f, XAS_angle_ripple, label='ripple', ls='--')\n", - "# plt.plot(f, fM_s*linb, label=r'linb $fM_s$')\n", - "# plt.plot(f, jnp.full_like(f, phifRef), label=r'$\\phi_{f_{ref}}$')\n", - "name = \"Angle\"\n", - "plt.legend()\n", - "plt.title(\n", - " r\"{} h22 ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$)\".format(\n", - " name, m1, m2, chi1, chi2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(f\"{name}\")\n", - "plt.xscale(\"log\")\n", - "# plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "57b1d99c", - "metadata": {}, - "outputs": [], - "source": [ - "N = 5\n", - "rng = np.random.default_rng(seed=42)\n", - "masses = rng.uniform(1.2, 30, (N,2))\n", - "spins = rng.uniform(0, 0.8, (N,2))" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "45318f35", - "metadata": {}, - "outputs": [], - "source": [ - "offsets = np.zeros(N**2)\n", - "ctr = 0 #yes I'm lazy\n", - "\n", - "for i, ms in enumerate(masses):\n", - " for j, ss in enumerate(spins):\n", - " m1, m2 = ms\n", - " chi1, chi2 = ss\n", - "\n", - " tc = 0.0 # Time of coalescence in seconds\n", - " phic = 0.0 # Time of coalescence\n", - " dist_mpc = 440 # Distance to source in Mpc -> don't really matter here\n", - " inclination = 0.0 # Inclination Angle -> don't really matter here\n", - "\n", - " q = m1/m2\n", - " Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", - " theta_ripple = jnp.array(\n", - " [\n", - " Mc,\n", - " eta,\n", - " chi1,\n", - " chi2,\n", - " dist_mpc,\n", - " tc,\n", - " phic,\n", - " inclination,\n", - " ]\n", - " )\n", - " \n", - " ### ripple computation ###\n", - " XAS_angle_ripple, lina, linb, fM_s, phifRef, psi4tostrain = gen_IMRPhenomXAS(fs_ripple, theta_ripple, f_ref, get_phase=True)\n", - "\n", - "\n", - " ### LAL computation ###\n", - " m1_kg = m1 * lal.MSUN_SI\n", - " m2_kg = m2 * lal.MSUN_SI\n", - " distance = dist_mpc * 1e6 * lal.PC_SI\n", - "\n", - " laldict_OnlyPhase = lal.CreateDict()\n", - "\n", - " # Specify we only want the phase contribution. This exits the function before any NRTidal contributions so don't use this to test tidal phase contributions!\n", - " lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", - "\n", - " LAL_OnlyPhase = lalsim.SimIMRPhenomXASGenerateFD(\n", - " m1_kg,\n", - " m2_kg,\n", - " chi1,\n", - " chi2,\n", - " distance,\n", - " f_l,\n", - " f_u,\n", - " df,\n", - " phic,\n", - " f_ref,\n", - " laldict_OnlyPhase,\n", - " )\n", - " XAS_angle_LAL = LAL_OnlyPhase.data.data[mask_lal]\n", - "\n", - " offset = XAS_angle_LAL[0] - XAS_angle_ripple[0]\n", - " offsets[ctr] += offset.real\n", - " ctr += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "bd500d45", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(np.arange(N**2), offsets, edgecolor='blue', alpha=0.5, label='data')\n", - "plt.axhline(PI, 0, N**2, ls='--', c='r', label=r'$\\pi$')\n", - "plt.ylabel(r'$\\Delta \\psi$')\n", - "plt.title(r'XAS $h_{22}$-mode phase offset')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2266917", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jim_dev_env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/test/NRTidalv3_testing.ipynb b/test/NRTidalv3_testing.ipynb deleted file mode 100644 index 6280d1f5..00000000 --- a/test/NRTidalv3_testing.ipynb +++ /dev/null @@ -1,1087 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "22705cfa", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/robinc/anaconda3/envs/jim_dev_env/lib/python3.12/site-packages/lalsimulation/lalsimulation.py:8: UserWarning: Wswiglal-redir-stdio:\n", - "\n", - "SWIGLAL standard output/error redirection is enabled in IPython.\n", - "This may lead to performance penalties. To disable locally, use:\n", - "\n", - "with lal.no_swig_redirect_standard_output_error():\n", - " ...\n", - "\n", - "To disable globally, use:\n", - "\n", - "lal.swig_redirect_standard_output_error(False)\n", - "\n", - "Note however that this will likely lead to error messages from\n", - "LAL functions being either misdirected or lost when called from\n", - "Jupyter notebooks.\n", - "\n", - "To suppress this warning, use:\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", - "import lal\n", - "\n", - " import lal\n" - ] - } - ], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "from ripplegw.constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN, C\n", - "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc, gen_IMRPhenomXAS_NRTidalv3\n", - "from ripplegw import ms_to_Mc_eta, lambdas_to_lambda_tildes, get_eff_pads, get_match_arr\n", - "\n", - "import lalsimulation as lalsim\n", - "import lal\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7bd3da0b", - "metadata": {}, - "outputs": [], - "source": [ - "# Frequency grid\n", - "T = 128\n", - "f_l = 20.0\n", - "f_sampling = 2 * 4096\n", - "f_u = f_sampling // 2\n", - "f_ref = f_l\n", - "\n", - "delta_t = 1 / f_sampling\n", - "tlen = int(round(T / delta_t))\n", - "freqs = np.fft.rfftfreq(tlen, delta_t)\n", - "df = freqs[1] - freqs[0]\n", - "fs = freqs[(freqs > f_l) & (freqs < f_u)]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3f33f2db", - "metadata": {}, - "outputs": [], - "source": [ - "m1 = 1.37 # In solar masses\n", - "m2 = 1.37\n", - "chi1 = 0.141 # Dimensionless spin\n", - "chi2 = 0.141\n", - "lambda1 = 1001.8\n", - "lambda2 = 1001.8\n", - "tc = 0.0 # Time of coalescence in seconds\n", - "phic = 0.0 # Time of coalescence\n", - "dist_mpc = 440 # Distance to source in Mpc\n", - "inclination = 0.0 # Inclination Angle\n", - "\n", - "q = m1/m2\n", - "Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2]))\n", - "lambda_tilde, delta_lambda_tilde = lambdas_to_lambda_tildes(\n", - " jnp.array([lambda1, lambda2, m1, m2])\n", - ")\n", - "\n", - "theta_ripple = jnp.array(\n", - " [\n", - " Mc,\n", - " eta,\n", - " chi1,\n", - " chi2,\n", - " lambda_tilde,\n", - " delta_lambda_tilde,\n", - " dist_mpc,\n", - " tc,\n", - " phic,\n", - " inclination,\n", - " ]\n", - ")\n", - "fs_ripple = jnp.arange(f_l, f_u, df)[1:]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a330c909", - "metadata": {}, - "outputs": [], - "source": [ - "from ripplegw.waveforms.NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper\n", - "from ripplegw.waveforms.IMRPhenomD_NRTidalv2 import get_planck_taper, get_tidal_amplitude, get_spin_phase_correction\n", - "from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc\n", - "from ripplegw.waveforms.IMRPhenom_tidal_utils import get_kappa" - ] - }, - { - "cell_type": "markdown", - "id": "c66750f6", - "metadata": {}, - "source": [ - "## Some inital sanity checks:" - ] - }, - { - "cell_type": "markdown", - "id": "be23d51e", - "metadata": {}, - "source": [ - "### Merger frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a65d3d67", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1623.969\n", - "1623.9690632395718\n" - ] - } - ], - "source": [ - "theta_intrinsic = jnp.array([m1, m2, chi1, chi2, lambda1, lambda2])\n", - "M = m1 + m2\n", - "Xa = m1/M\n", - "f_merger_ripple = _get_merger_frequency(theta_intrinsic)\n", - "print(f_merger_ripple)\n", - "f_merger_LAL = lalsim.SimNRTunedTidesMergerFrequency_v3(M, lambda1, lambda2, q, chi1, chi2)\n", - "print(f_merger_LAL)" - ] - }, - { - "cell_type": "markdown", - "id": "36d14995", - "metadata": {}, - "source": [ - "### Tidal phase components" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c1dbdc6b", - "metadata": {}, - "outputs": [], - "source": [ - "PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic)\n", - "NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs)\n", - "\n", - "M_s = M * gt\n", - "M_omega = (PI * M_s * fs_ripple)\n", - "P_P = general_planck_taper(fs, 1.15*f_merger_ripple, 1.35*f_merger_ripple)\n", - "\n", - "kappa = get_kappa(jnp.array([m1,m2,chi1,chi2,lambda1,lambda2]))\n", - "tidal_amp = get_tidal_amplitude(M_omega**(2/3), theta_intrinsic, kappa, distance=dist_mpc)\n", - "\n", - "tidal_phase = get_tidal_phase(M_omega, NRTidalv3_coeffs, PN_coeffs)\n", - "tidal_phasePN = get_tidal_phase_PN(M_omega, Xa, lambda1, lambda2, PN_coeffs)\n", - "tidal_phase_corr = tidal_phase * (1 - P_P) + tidal_phasePN * P_P" - ] - }, - { - "cell_type": "markdown", - "id": "aca185ff", - "metadata": {}, - "source": [ - "The following cells are a test for the local minimum of the tidal phase. It doesn't work yet, so don't pay too much attention to it" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "610906ab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[False False False ... False True False]\n", - "215839\n", - "True\n", - "[False False False ... True True True]\n" - ] - } - ], - "source": [ - "increasing = jnp.concatenate([jnp.array([False]), tidal_phase[1:] >= tidal_phase[:-1]])\n", - "fHzmrgcheck = 0.9*f_merger_ripple\n", - "# Apply fHz threshold\n", - "valid = (fs >= fHzmrgcheck) & increasing\n", - "print(valid)\n", - "first_valid = jnp.argmax(valid) # returns 0 if none are True\n", - "print(first_valid)\n", - "found = jnp.any(valid)\n", - "print(found)\n", - "tidal_min_value = jnp.where(found, tidal_phase[first_valid - 1], 0.0)\n", - "\n", - "# Build mask for indices after that point\n", - "mask = (jnp.arange(fs.size) > (first_valid - 1)) & found\n", - "print(mask)\n", - "phi_tidal = jnp.where(mask, tidal_min_value, tidal_phase)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f842e550", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(fs, tidal_phase,label='Phase corr')\n", - "plt.plot(fs, tidal_phasePN,label='PN corr')\n", - "plt.plot(fs, tidal_phase_corr, ls='--',label='Full corr')\n", - "plt.legend()\n", - "plt.xlabel('f')\n", - "# plt.xscale('log')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9d8657b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tidal_phase_corr_withMIN = phi_tidal * (1 - P_P) + tidal_phasePN * P_P\n", - "plt.plot(fs, phi_tidal,label='Phase corr')\n", - "plt.plot(fs, tidal_phasePN,label='PN corr')\n", - "plt.plot(fs, tidal_phase_corr_withMIN, ls='--',label='Full corr')\n", - "plt.legend()\n", - "plt.xlabel('f')\n", - "# plt.xscale('log')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d943c569", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "laldict = lal.CreateDict()\n", - "lalsim.SimInspiralWaveformParamsInsertTidalLambda1(laldict, lambda1)\n", - "lalsim.SimInspiralWaveformParamsInsertTidalLambda2(laldict, lambda2)\n", - "quad1 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda1)\n", - "quad2 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda2)\n", - "# print(quad1)\n", - "oct1 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad1)\n", - "oct2 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad2)\n", - "# print(oct1)\n", - "lalsim.SimInspiralWaveformParamsInsertdQuadMon1(laldict, quad1 - 1)\n", - "lalsim.SimInspiralWaveformParamsInsertdQuadMon2(laldict, quad2 - 1)\n", - "\n", - "IMRphenom = \"IMRPhenomXAS_NRTidalv3\"\n", - "approximant = lalsim.SimInspiralGetApproximantFromString(IMRphenom)\n", - "\n", - "theta = np.array(\n", - " [m1, m2, chi1, chi2, lambda1, lambda2, dist_mpc, tc, phic, inclination]\n", - ")\n", - "\n", - "m1_kg = theta[0] * lal.MSUN_SI\n", - "m2_kg = theta[1] * lal.MSUN_SI\n", - "distance = dist_mpc * 1e6 * lal.PC_SI\n", - "\n", - "ph_tdLAL = np.zeros(fs.shape)\n", - "amp_tdLAL = np.zeros(fs.shape)\n", - "pl_taperLAL = np.zeros(fs.shape)\n", - "\n", - "lalsim.SimNRTunedTidesFDTidalPhaseFrequencySeries(\n", - " phi_tidal=ph_tdLAL,\n", - " amp_tidal=amp_tdLAL,\n", - " planck_taper=pl_taperLAL,\n", - " fHz=fs,\n", - " m1_SI=m1_kg,\n", - " m2_SI=m2_kg,\n", - " lambda1=lambda1,\n", - " lambda2=lambda2,\n", - " chi1=chi1,\n", - " chi2=chi2,\n", - " NRTidal_version=lalsim.NRTidalv3_V\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c3339856", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(fs, ph_tdLAL, label='LAL', lw=2.5)\n", - "plt.plot(fs, tidal_phase_corr, label='ripple no min', ls='--')\n", - "plt.plot(fs, tidal_phase_corr_withMIN, label='ripple with min', ls='--')\n", - "# plt.axvline(fs[first_valid], min(ph_tdLAL), max(ph_tdLAL), c='r', ls='-.', alpha=0.7)S\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "da761f90", - "metadata": {}, - "source": [ - "### NRTidal phase component\n", - "(not spin!)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fcfeb8b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 5))\n", - "delta_psi_tidal = ph_tdLAL - tidal_phase_corr\n", - "delta_psi_tidal_withMIN = ph_tdLAL - tidal_phase_corr_withMIN\n", - "plt.plot(fs, abs(delta_psi_tidal), \"-\", color=\"black\", label='No min')\n", - "plt.plot(fs, abs(delta_psi_tidal_withMIN), \"-\", color=\"orange\", alpha=0.6, label='With min')\n", - "plt.axvline(1.15 * f_merger_ripple, linestyle=\"--\", color=\"r\", label=\"Planck taper\")\n", - "plt.axvline(1.35 * f_merger_ripple, linestyle=\"--\", color=\"r\")\n", - "plt.legend()\n", - "plt.title(\n", - " r\"Tidal phase difference ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(r\"$|\\Delta \\psi^{NRT3a}_T|$\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5, 1.2 * f_merger_ripple + 20)\n", - "plt.yscale(\"log\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "51a87712", - "metadata": {}, - "source": [ - "### $\\kappa^{2T}$ check" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6a2ca110", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "187.8375\n", - "187.8375\n" - ] - } - ], - "source": [ - "kappa_LAL = lalsim.SimNRTunedTidesComputeKappa2T(m1_kg, m2_kg, lambda1, lambda2)\n", - "print(kappa_LAL)\n", - "print(kappa)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bd08c356", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([ 5.5581057e-24-2.7243789e-24j, 8.2296243e-25+6.1320864e-24j,\n", - " -6.0873832e-24-1.0901924e-24j, ..., 0.0000000e+00+0.0000000e+00j,\n", - " 0.0000000e+00+0.0000000e+00j, 0.0000000e+00+0.0000000e+00j], dtype=complex64)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Ripple waveform generation\n", - "hp_ripple, hc_ripple = gen_IMRPhenomXAS_NRTidalv3_hphc(\n", - " fs_ripple, theta_ripple, f_ref\n", - ")\n", - "hp_ripple" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5616c838", - "metadata": {}, - "outputs": [], - "source": [ - "hp, _ = lalsim.SimInspiralChooseFDWaveform(\n", - " m1_kg,\n", - " m2_kg,\n", - " 0.0,\n", - " 0.0,\n", - " chi1,\n", - " 0.0,\n", - " 0.0,\n", - " chi2,\n", - " distance,\n", - " inclination,\n", - " phic,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " df,\n", - " f_l,\n", - " f_u,\n", - " f_ref,\n", - " laldict,\n", - " approximant,\n", - ")\n", - "\n", - "approximantXAS = lalsim.SimInspiralGetApproximantFromString(\"IMRphenomXAS\")\n", - "laldictXAS = lal.CreateDict()\n", - "hpXAS, _ = lalsim.SimInspiralChooseFDWaveform(\n", - " m1_kg,\n", - " m2_kg,\n", - " 0.0,\n", - " 0.0,\n", - " chi1,\n", - " 0.0,\n", - " 0.0,\n", - " chi2,\n", - " distance,\n", - " inclination,\n", - " phic,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " df,\n", - " f_l,\n", - " f_u,\n", - " f_ref,\n", - " laldictXAS,\n", - " approximantXAS,\n", - ")\n", - "\n", - "freqs_lal = np.arange(len(hp.data.data)) * df\n", - "\n", - "mask_lal = (freqs_lal > f_l) & (freqs_lal < f_u)\n", - "hp_lalsuite = hp.data.data[mask_lal]\n", - "hpXAS_lalsuite = hpXAS.data.data[mask_lal]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "05c1e340", - "metadata": {}, - "outputs": [], - "source": [ - "f = freqs_lal[mask_lal]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "0f7b849c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = 0.75\n", - "plt.subplots(1, 1, figsize=(17, 7))\n", - "# plt.subplot(1, 2, 1)\n", - "\n", - "plt.plot(f, hp_lalsuite.real, \"-\", label=\"LAL\", alpha=a)\n", - "plt.plot(f, hp_ripple.real, \"-\", label=\"ripple\", alpha=a)\n", - "# plt.plot(fs, hpXAS_lalsuite.real, label=\"LAL XAS\", alpha=a)\n", - "plt.title(f\"h+, NRTidalv3 lambda1, lambda2 = {lambda1, lambda2}\")\n", - "plt.title(\n", - " r\"$h_+$ NRTidalv3 ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(chi1,chi2,lambda1, lambda2)\n", - ")\n", - "plt.xlabel(\"Frequency\")\n", - "plt.ylabel(\"Strain\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5)\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b7ab1868", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 5.5581057e-24-2.7243789e-24j 8.2296243e-25+6.1320864e-24j\n", - " -6.0873832e-24-1.0901924e-24j ... 0.0000000e+00+0.0000000e+00j\n", - " 0.0000000e+00+0.0000000e+00j 0.0000000e+00+0.0000000e+00j]\n", - "[ 5.55985471e-24-2.72080888e-24j 8.22331359e-25+6.13217153e-24j\n", - " -6.08430890e-24-1.10723009e-24j ... 0.00000000e+00+0.00000000e+00j\n", - " 0.00000000e+00+0.00000000e+00j 0.00000000e+00+0.00000000e+00j]\n" - ] - } - ], - "source": [ - "print(hp_ripple)\n", - "print(hp_lalsuite)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c266abf6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "43.136898\n", - "43.13691\n", - "nan\n" - ] - } - ], - "source": [ - "psd_file = \"psds/psd.txt\"\n", - "f_ASD, ASD = np.loadtxt(psd_file, unpack=True)\n", - "ASD = np.sqrt(ASD)\n", - "\n", - "ASD_vals = np.interp(fs, f_ASD, ASD)\n", - "PSD_vals = ASD_vals**2\n", - "pad_low, pad_high = get_eff_pads(fs)\n", - "# match = get_match_arr(\n", - "# pad_low,\n", - "# pad_high,\n", - "# PSD_vals,\n", - "# hp_ripple,\n", - "# hp_lalsuite,\n", - "# )\n", - "\n", - "h1s = hp_ripple\n", - "h2s = hp_lalsuite\n", - "\n", - "norm1 = jnp.sqrt(jnp.sum((jnp.abs(h1s)/ASD_vals)**2))\n", - "norm2 = jnp.sqrt(jnp.sum((jnp.abs(h2s)/ASD_vals)**2))\n", - "\n", - "print(norm1)\n", - "print(norm2)\n", - "# Use IFFT trick to maximize over t_c. Ref: Maggiore's book, eq. 7.171.\n", - "integrand_padded = jnp.concatenate((pad_low, h1s.conj() * h2s / PSD_vals, pad_high))\n", - "match = jnp.abs(len(integrand_padded) * jnp.fft.ifft(integrand_padded)).max() / (norm1 * norm2)\n", - "\n", - "print(match)" - ] - }, - { - "cell_type": "markdown", - "id": "b1a7a0d9", - "metadata": {}, - "source": [ - "## Check total amplitude and phase" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "5940b64e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Get the merger frequency to check the Planck taper window\n", - "\n", - "l1, l2 = lambda1, lambda2\n", - "merger_params = jnp.array([m1, m2, chi1, chi2, l1, l2])\n", - "\n", - "f_merger = f_merger_ripple\n", - "f_merger = float(f_merger)\n", - "\n", - "## Get amplitude and angle for comparison\n", - "\n", - "A_lalsuite = jnp.abs(hp_lalsuite)\n", - "angle_lalsuite = np.unwrap(np.angle(hp_lalsuite))\n", - "phase_lalsuite = hp_lalsuite / A_lalsuite\n", - "\n", - "A_ripple = jnp.abs(hp_ripple)\n", - "angle_ripple = np.unwrap(np.angle(hp_ripple))\n", - "phase_ripple = hp_ripple / A_ripple\n", - "\n", - "# Choose whether we plot the angle or the phase\n", - "plot_angle = True\n", - "\n", - "plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n", - "plt.subplot(2, 1, 1)\n", - "\n", - "# Plot the amplitude\n", - "plt.plot(freqs_lal[mask_lal], A_lalsuite, \"-\", label=\"LAL\")\n", - "plt.plot(fs_ripple, A_ripple, \"--\", label=\"ripple\")\n", - "\n", - "# Plot where the Planck taper has to take place\n", - "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", - "\n", - "plt.title(\n", - " r\"Amplitudes $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.yscale(\"log\")\n", - "plt.xscale(\"log\")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(\"Amplitude\")\n", - "plt.ylim(1e-27, 1e-21)\n", - "plt.xlim(f_l - 5)\n", - "plt.legend()\n", - "\n", - "# Plot the angle or the phase\n", - "plt.subplot(2, 1, 2)\n", - "if plot_angle:\n", - " plt.plot(freqs_lal[mask_lal], angle_lalsuite, \"-\", label=\"LAL\")\n", - " plt.plot(fs_ripple, angle_ripple, \"--\", label=\"ripple\")\n", - " name = \"Angle\"\n", - "else:\n", - " plt.plot(freqs_lal[mask_lal], phase_lalsuite, \"-\", label=\"LAL\")\n", - " plt.plot(fs_ripple, phase_ripple, \"--\", label=\"ripple\")\n", - " name = \"Phase\"\n", - "plt.legend()\n", - "plt.title(\n", - " r\"{} $h_+$ ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " name, m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.ylabel(f\"{name}\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ea95f1b6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 5))\n", - "plt.plot(f, abs((angle_lalsuite - angle_ripple)/angle_lalsuite), \"-\", color=\"black\", label='Total relative error')\n", - "plt.plot(fs, abs(delta_psi_tidal/ph_tdLAL), \"-\", label=r\"$|\\Delta \\psi^{NRT3a}_T / \\psi_T^{LAL}|$\")\n", - "plt.legend()\n", - "plt.title(\n", - " r\"Angle error contributions ($m_1, m_2) = ({}, {}$) ($\\chi_1, \\chi_2) = ({}, {}$) ($\\Lambda_1, \\Lambda_2) = ({}, {}$)\".format(\n", - " m1, m2, chi1, chi2, lambda1, lambda2\n", - " )\n", - ")\n", - "plt.yscale('log')\n", - "plt.axvline(f_merger, linestyle=\"--\", color=\"black\", label=\"Planck taper\")\n", - "plt.axvline(1.2 * f_merger, linestyle=\"--\", color=\"black\")\n", - "plt.xlabel(\"Frequency (Hz)\")\n", - "plt.xscale(\"log\")\n", - "plt.xlim(f_l - 5, 1.2 * f_merger + 20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "da2b0f0f", - "metadata": {}, - "outputs": [], - "source": [ - "Xb = 1-Xa\n", - "SS_3p5PN, SSS_3p5PN = lalsim.SimInspiralGetHOSpinTerms(\n", - " Xa,\n", - " Xb,\n", - " chi1,\n", - " chi2,\n", - " quad1,\n", - " quad2\n", - ")\n", - "\n", - "SS_3p5PN *= 3.*M_omega**(2/3)/(128.*eta)\n", - "SSS_3p5PN *= 3.*M_omega**(2/3)/(128.*eta)" - ] - }, - { - "cell_type": "markdown", - "id": "d5be7ab4", - "metadata": {}, - "source": [ - "## Phase Checks\n", - "Let's extract the BBH phase after it has been aligned with the tidal corrections, but no global tidal corrections have been made. So only the alignment has been corrected. This is something we can extract from LAL" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "2d659e2e", - "metadata": {}, - "outputs": [], - "source": [ - "laldict_OnlyPhase = lal.CreateDict()\n", - "lalsim.SimInspiralWaveformParamsInsertTidalLambda1(laldict_OnlyPhase, lambda1)\n", - "lalsim.SimInspiralWaveformParamsInsertTidalLambda2(laldict_OnlyPhase, lambda2)\n", - "quad1 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda1)\n", - "quad2 = lalsim.SimUniversalRelationQuadMonVSlambda2Tidal(lambda2)\n", - "# print(quad1)\n", - "oct1 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad1)\n", - "oct2 = lalsim.SimUniversalRelationSpinInducedOctupoleVSSpinInducedQuadrupole(quad2)\n", - "# print(oct1)\n", - "lalsim.SimInspiralWaveformParamsInsertdQuadMon1(laldict_OnlyPhase, quad1 - 1)\n", - "lalsim.SimInspiralWaveformParamsInsertdQuadMon2(laldict_OnlyPhase, quad2 - 1)\n", - "\n", - "# Specify we only want phase contribution (before LAL adds tidal and spin phase terms)\n", - "lalsim.SimInspiralWaveformParamsInsertPhenomXOnlyReturnPhase(laldict_OnlyPhase, 1)\n", - "\n", - "phaseLAL = lalsim.SimIMRPhenomXASGenerateFD(\n", - " m1_kg,\n", - " m2_kg,\n", - " chi1,\n", - " chi2,\n", - " distance,\n", - " f_l,\n", - " f_u,\n", - " df,\n", - " phic,\n", - " f_ref,\n", - " laldict_OnlyPhase,\n", - ")\n", - "\n", - "phaseLAL = phaseLAL.data.data[mask_lal]\n", - "\n", - "h22 = lalsim.SimIMRPhenomXASGenerateFD(\n", - " m1_kg,\n", - " m2_kg,\n", - " chi1,\n", - " chi2,\n", - " distance,\n", - " f_l,\n", - " f_u,\n", - " df,\n", - " phic,\n", - " f_ref,\n", - " laldict,\n", - ")\n", - "\n", - "h22 = h22.data.data[mask_lal]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "1bc242c1", - "metadata": {}, - "outputs": [], - "source": [ - "psi_bbh, phase_shift, phifRef, phiTfRef, dphiXAS, dphiT, dphi_merger, psi_T, psi_SS = gen_IMRPhenomXAS_NRTidalv3(f, theta_ripple, f_ref, get_phase=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "88e0b3b2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0024134202\n", - "-0.036544297\n" - ] - } - ], - "source": [ - "print(dphiXAS)\n", - "print(dphiT)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a7ae5689", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(f, phaseLAL.real, label=r'$\\psi_{tot}^{LAL}$')\n", - "plt.plot(f, psi_bbh + phase_shift, ls='--', label=r'$\\psi_{BBH} + \\psi_{fref}$')\n", - "# plt.plot(f, phase_shift, ls='--', label=r'$\\psi_{fref}$')\n", - "plt.plot(f, psi_T, ls='--', label=r'$\\psi_T$')\n", - "plt.plot(f, psi_SS, ls='--', label=r'$\\psi_S$')\n", - "plt.legend()\n", - "# plt.xscale('log')\n", - "# plt.yscale('log')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6485586b", - "metadata": {}, - "source": [ - "$\\psi_T$ won't play a role here as this is not extracted from LAL. Let's look further into the difference between the tidal-aligned BBH baselines" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "37a2cbc6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(f, (phaseLAL.real - (psi_bbh + phase_shift)))\n", - "# plt.xscale('log')\n", - "# plt.yscale('log')\n", - "plt.title(r'NRTidalv3-aligned $\\Delta (\\psi_{XAS} + \\delta \\psi_{T})$')\n", - "plt.ylabel(r'$\\Delta \\psi$')\n", - "plt.xlabel('f (Hz)')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "2d8f39ce", - "metadata": {}, - "source": [ - "This error increases linearly with frequency. The only term that scales linearly with frequency in the tidal-aligned baseline is linb*Mf (line 840 in LALSimIMRPhenomX.c). So this is probably where the alignment error lies." - ] - }, - { - "cell_type": "markdown", - "id": "8cf262e7", - "metadata": {}, - "source": [ - "Currently, the phase that we get from **LAL** consists of the non-algned XAS BBH baseline + the tidal alignment:\n", - "$$\\psi^{LAL} = \\psi_{XAS}(f) - [\\psi_{XAS}(f_{ref}) - \\psi_T(f_{ref}) - d\\psi_{XAS}(f_{ref}) M_s f_{ref} + d\\psi_T(f_{ref}) M_s f_{ref}] + 2\\psi_0 + \\frac{\\pi}{4} + b M_s f$$\n", - "where $M_s$ is the total mass of the system in seconds. *If I am not mistaken*, b should be given by: $-(d\\psi_{XAS}(f_{ref}) - d\\psi_T(f_{ref}))$ (see lines 737-739 of LALSimIMRPhenomX.c)\n", - "\n", - "Considering the ripple and LAL XAS baselines agree well, we can use the non-phase-aligned ripple XAS phase to remove this baseline and directly compare the ripple and LAL tidal alignment corrections." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e6521469", - "metadata": {}, - "outputs": [], - "source": [ - "from ripplegw.waveforms.IMRPhenomXAS import Phase\n", - "from ripplegw.waveforms.IMRPhenomX_utils import PhenomX_phase_coeff_table as bbh_phase_coeffs\n", - "from ripplegw.waveforms import IMRPhenomX_utils\n", - "from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import fullTidalPhaseCorrection" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d0a925a3", - "metadata": {}, - "outputs": [], - "source": [ - "ripple_XAS_phase = Phase(f, jnp.array([m1,m2,chi1,chi2]), bbh_phase_coeffs)\n", - "LAL_phase_offset = phaseLAL.real - ripple_XAS_phase # Should consist of linb*Mf + lina(=0) + phifRef (LAL!!)\n", - "\n", - "# b * M * (f - f_ref) = \\Delta\\psi + phifRef(ripple) - phiTfRef\n", - "bMf_min_fref = LAL_phase_offset + phifRef - phiTfRef # phiTfRef is defined with opposite sign from LAL" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "586a19ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(f, bMf_min_fref, label=r'$M_s(f-f_{ref})\\text{linb}$')\n", - "plt.plot(f, dphi_merger*M_s*(f - f_ref), ls='--', label=r'$M_s(f-f_{ref})d\\psi_{merger}$')\n", - "plt.legend()\n", - "# plt.yscale('log')\n", - "# plt.xscale('log')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9ab61483", - "metadata": {}, - "source": [ - "This is good enough for now" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6923a6d2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "jim_dev_env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/test/benchmark_waveform.py b/test/benchmark_waveform.py index 17481b4b..35c95314 100644 --- a/test/benchmark_waveform.py +++ b/test/benchmark_waveform.py @@ -159,7 +159,7 @@ def random_match(n: int, bounds: dict, IMRphenom: str = "IMRPhenomD_NRTidalv2", thetas = np.array(thetas) matches = np.array(matches) if outdir is not None: - csv_name = f"{outdir}/matches_{IMRphenom}_2.csv" + csv_name = f"{outdir}/matches_{IMRphenom}_withMIN.csv" print(f"Saving matches to {csv_name}") df = save_matches(csv_name, thetas, matches, is_tidal=is_tidal, verbose=True) From e62f270e4c3ff492b31c48099ff811dc671ffb89 Mon Sep 17 00:00:00 2001 From: Thomas Ng Date: Fri, 21 Nov 2025 13:11:04 +0100 Subject: [PATCH 032/474] Add Dependabot configuration for GitHub Actions updates --- .github/dependabot.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..f53fb3f8 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,10 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + day: "sunday" + target-branch: "ripple-dev" + commit-message: + prefix: "ci" From 1f9fb777d8608b11ef9816caa4352bd779a868fb Mon Sep 17 00:00:00 2001 From: robkamcha Date: Tue, 25 Nov 2025 11:20:57 +0100 Subject: [PATCH 033/474] Added local minimum check and analytical tidal phase derivative --- .../waveforms/IMRPhenomXAS_NRTidalv3.py | 228 +++++++++++++++++- test/benchmark_waveform.py | 44 +++- 2 files changed, 248 insertions(+), 24 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py index e9f9d40c..31d7ce75 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS_NRTidalv3.py @@ -5,7 +5,7 @@ from ..constants import gt, PI from ..typing import Array from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas -from .IMRPhenom_tidal_utils import get_kappa +from .IMRPhenom_tidal_utils import get_kappa, get_quadparam_octparam from .IMRPhenomD_NRTidalv2 import get_spin_phase_correction, get_planck_taper, get_tidal_amplitude # Same between v2 and v3 from .NRTidalv3_utils import _get_merger_frequency, get_tidal_phase, get_NRTidalv3_coefficients, get_tidalphasePN_coeffs, get_tidal_phase_PN, general_planck_taper, fullTidalPhaseCorrection, changePhase_if_min from ripplegw.waveforms import IMRPhenomX_utils @@ -61,12 +61,16 @@ def _gen_IMRPhenomXAS_NRTidalv3( if no_taper: P_P = jnp.ones_like(f) P_P_fref = 1.0 - P_P_ffinal = 1.0 + # P_P_ffinal = 1.0 + dphiT = jax.grad(fullTidalPhaseCorrection)(f_final*M_s, theta_intrinsic, 1.0) A_P = jnp.ones_like(f) else: P_P = general_planck_taper(f, 1.15*f_merger, 1.35*f_merger) - P_P_fref = general_planck_taper(f_ref, 1.15*f_merger, 1.35*f_merger) - P_P_ffinal = general_planck_taper(f_final, 1.15*f_merger, 1.35*f_merger) + P_P_fref = general_planck_taper(f_ref*M_s, 1.15*f_merger*M_s, 1.35*f_merger*M_s) + # P_P_ffinal = general_planck_taper(f_final, 1.15*f_merger, 1.35*f_merger) + dphiT = jax.grad(lambda fMs: fullTidalPhaseCorrection( + fMs, theta_intrinsic, general_planck_taper(fMs, 1.15*f_merger*M_s, 1.35*f_merger*M_s) + ))(f_final*M_s) A_P = get_planck_taper(f, f_merger) bbh_phase_coeffs = IMRPhenomX_utils.PhenomX_phase_coeff_table @@ -75,11 +79,11 @@ def _gen_IMRPhenomXAS_NRTidalv3( Phase(f_ref, theta_intrinsic[:4], bbh_phase_coeffs) - PI / 4.0 ) # This is part of the BBH phase alignment - phiTfRef = fullTidalPhaseCorrection(f_ref, theta_intrinsic, P_P_fref) # This is part of the tidal correction to the phase alignment + phiTfRef = fullTidalPhaseCorrection(f_ref*M_s, theta_intrinsic, P_P_fref) # This is part of the tidal correction to the phase alignment - dphiXAS = jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs) - dphiT = jax.grad(fullTidalPhaseCorrection)(f_final, theta_intrinsic, P_P_ffinal) - dphi_merger = -(dphiXAS - dphiT) / M_s # linb from LAL + dphiXAS = jax.grad(Phase)(f_final, theta_intrinsic[:4], bbh_phase_coeffs) / M_s + # dphiT = IMRPhenomX_TidalPhaseDerivative(f_final, theta_intrinsic) # Analytical derivative for testing purposes + dphi_merger = -(dphiXAS - dphiT) # linb from LAL ext_phase_contrib = 2.0 * PI * f * theta_extrinsic[1] + 2 * theta_extrinsic[2] @@ -90,12 +94,13 @@ def _gen_IMRPhenomXAS_NRTidalv3( NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) NRTidalv3_phase = get_tidal_phase(x, NRTidalv3_coeffs, PN_coeffs) - # TODO: Check for local minimum -> this doesn't seem to work correctly at the moment + # Check for local minimum fHzmrgcheck = 0.9 * f_merger increasing = jnp.concatenate([jnp.array([False]), NRTidalv3_phase[1:] >= NRTidalv3_phase[:-1]]) valid = (f >= fHzmrgcheck) & increasing - # if local minimum found: set NRTidalv3 phase to this value afterwards + # If local minimum found: set NRTidalv3 phase to this value afterwards + # See discussion in Sec. IV G of arXiv:2311.07456 x_lax = (f, NRTidalv3_phase, valid) NRTidalv3_phase = jax.lax.cond( jnp.any(valid), @@ -125,7 +130,7 @@ def gen_IMRPhenomXAS_NRTidalv3( Generate NRTidalv3 frequency domain waveform following 2311.07456. params array contains both intrinsic and extrinsic variables - theta = [Mchirp, eta, chi1, chi2, D, tc, phic]: + theta = [Mchirp, eta, chi1, chi2, lambda1, lambda2, D, tc, phic]: Mchirp: Chirp mass of the system [solar masses] eta: Symmetric mass ratio [between 0.0 and 0.25] chi1: Dimensionless aligned spin of the primary object [between -1 and 1] @@ -210,3 +215,204 @@ def gen_IMRPhenomXAS_NRTidalv3_hphc( hc = -1j * h0 * jnp.cos(iota) return hp, hc + + +################################### ANALYTICAL DERIVATIVE FOR TESTING ################################### + +def IMRPhenomX_TidalPhaseDerivative(f, theta): + """Calculate tidal phase derivative at a single frequency f.""" + theta_intrinsic = theta + m1, m2, chi1, chi2, lambda1, lambda2 = theta_intrinsic + M = m1 + m2 + M_s = (m1 + m2) * gt + Mf = f * M_s + x = PI * f * M_s + + X_A = m1/M + X_B = m2/M + + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + + # Compute quadrupole parameters + quadparam1, octparam1 = get_quadparam_octparam(lambda1) + quadparam2, octparam2 = get_quadparam_octparam(lambda2) + + # Remove 1 for the BBH baseline, from here on, quadparam is "quadparam hat" as referred to in the NRTidalv2 paper etc + quadparam1 -= 1 + quadparam2 -= 1 + octparam1 -= 1 + octparam2 -= 1 + + c2pn = -50.0 * quadparam1 * chi1**2 * X_A**2 - 50.0 * quadparam2 * chi2**2 * X_B**2 + c3pn = ( + 5.0 + / 84.0 + * (9407.0 + 8218.0 * X_A - 2016.0 * X_A**2) + * quadparam1 + * X_A**2 + * chi1**2 + + 5.0 + / 84.0 + * (9407.0 + 8218.0 * X_B - 2016.0 * X_B**2) + * quadparam2 + * X_B**2 + * chi2**2 + ) + c3p5pn = ( + -400.0 * PI * quadparam1 * chi1**2 * X_A**2 + - 400.0 * PI * quadparam2 * chi2**2 * X_B**2 + ) + ( + 10.0 + * ((X_A**2 + 308.0 / 3.0 * X_A) * chi1 + (X_B**2 - 89.0 / 3.0 * X_B) * chi2) + * quadparam1 + * X_A**2 + * chi1**2 + + 10.0 + * ((X_B**2 + 308.0 / 3.0 * X_B) * chi2 + (X_A**2 - 89.0 / 3.0 * X_A) * chi1) + * quadparam2 + * X_B**2 + * chi2**2 + - 440.0 * octparam1 * X_A * X_A**2 * chi1**2 * chi1 + - 440.0 * octparam2 * X_B * X_B**2 * chi2**2 * chi2 + ) + + NRTuned_dphase=0. + + pfaN=3./(128.*X_A*X_B) + + threePN_dphase=(pfaN*(-c2pn + c3pn*x**(2.0/3.0)))/(3.0*Mf**(4.0/3.0) * PI**(1.0/3.0)) + + dphase=threePN_dphase + + dphase+=(2.*c3p5pn*pfaN*PI**(2.0/3.0))/(3.0*Mf**(1.0/3.0)) + + PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) + NRTidalv3_coeffs = get_NRTidalv3_coefficients(theta_intrinsic, PN_coeffs) + + s1 = NRTidalv3_coeffs[0] + s2 = NRTidalv3_coeffs[1] + s3 = NRTidalv3_coeffs[2] + + s2s3 = s2*s3 + s2Mf = -s2*Mf*PI*2.0 + exps2s3 = jnp.cosh(s2s3) + jnp.sinh(s2s3) + exps2Mf = jnp.cosh(s2Mf) + jnp.sinh(s2Mf) + + # Rewriting Eq. (27) of arXiv:2311.07456 + dynk2barfunc = 1.0 + ((s1) - 1)*(1.0/(1.0 + exps2Mf*exps2s3)) - ((s1-1.0)/(1.0 + exps2s3)) - 2.0*(Mf*PI)*((s1) - 1)*s2*exps2s3/((1.0 + exps2s3)*(1.0 + exps2s3)) + + kappaA = NRTidalv3_coeffs[4] + kappaB = NRTidalv3_coeffs[5] + + dynkappaA = kappaA*dynk2barfunc + dynkappaB = kappaB*dynk2barfunc + + # Pade Coefficients, Table II of arXiv:2311.07456 */ + n_5over2A = NRTidalv3_coeffs[6] + n_3A = NRTidalv3_coeffs[7] + d_1A = NRTidalv3_coeffs[8] + + n_5over2B = NRTidalv3_coeffs[9] + n_3B = NRTidalv3_coeffs[10] + d_1B = NRTidalv3_coeffs[11] + + # 7.5PN Coefficients */ + c_NewtA = PN_coeffs[0] + c_1A = PN_coeffs[1] + c_3over2A = PN_coeffs[2] + c_2A = PN_coeffs[3] + c_5over2A = PN_coeffs[4] + + c_NewtB = PN_coeffs[5] + c_1B = PN_coeffs[6] + c_3over2B = PN_coeffs[7] + c_2B = PN_coeffs[8] + c_5over2B = PN_coeffs[9] + + # Pade Coefficients constrained with PN, see Eq. (33) in arXiv:2311.07456 */ + n_1A = NRTidalv3_coeffs[12] + n_3over2A = NRTidalv3_coeffs[13] + n_2A = NRTidalv3_coeffs[14] + d_3over2A = NRTidalv3_coeffs[15] + + n_1B = NRTidalv3_coeffs[16] + n_3over2B = NRTidalv3_coeffs[17] + n_2B = NRTidalv3_coeffs[18] + d_3over2B = NRTidalv3_coeffs[19] + + # Rewriting Eq. (30) and (32) in arXiv:2311.07456 in terms of Mf instead of x */ + NRphasetermA=-((c_NewtA*dynkappaA*x**(5.0/3.0)*(1 + x**(2.0/3.0)*n_1A + Mf*n_3over2A*PI + x**(4.0/3.0)*n_2A + x**(5.0/3.0)*n_5over2A + x**2 * n_3A))/((1 + d_1A*x**(2.0/3.0) + d_3over2A*Mf*PI ))) + NRphasetermB=-((c_NewtB*dynkappaB*x**(5.0/3.0)*(1 + x**(2.0/3.0)*n_1B + Mf*n_3over2B*PI + x**(4.0/3.0)*n_2B + x**(5.0/3.0)*n_5over2B + x**2 * n_3B))/((1 + d_1B*x**(2.0/3.0) + d_3over2B*Mf*PI ))) + + NRphaseNRT = NRphasetermA + NRphasetermB + + PNtidalphaseA = -((c_NewtA*kappaA*x**(5.0/3.0)*(1 + x**(2.0/3.0)*c_1A + Mf*c_3over2A*PI + x**(4.0/3.0)*c_2A + x**(5.0/3.0)*c_5over2A))) + PNtidalphaseB = -((c_NewtB*kappaB*x**(5.0/3.0)*(1 + x**(2.0/3.0)*c_1B + Mf*c_3over2B*PI + x**(4.0/3.0)*c_2B + x**(5.0/3.0)*c_5over2B))) + + PNtidalphase = PNtidalphaseA + PNtidalphaseB + + #DERIVATIVES + + s2s3b = -s2*s3 + s2Mfb = s2*Mf*PI*2.0 + exps2s3b = jnp.cosh(s2s3b) + jnp.sinh(s2s3b) + exps2Mfb = jnp.cosh(s2Mfb) + jnp.sinh(s2Mfb) + + dynk2barfunc_deriv = (s1-1.0)*(2.0*PI)*s2*exps2Mfb*exps2s3b*(1/((1 + exps2Mfb*exps2s3b)*(1 + exps2Mfb*exps2s3b))) -2.0*PI*((s1) - 1.0)*s2*exps2s3/((1.0 + exps2s3)*(1.0 + exps2s3)) + + NRTuned_dphaseA1=-((c_NewtA*kappaA*dynk2barfunc_deriv*x**(5.0/3.0)*(1 + x**(2.0/3.0)*n_1A + Mf*n_3over2A*PI + x**(4.0/3.0)*n_2A + x**(5.0/3.0)*n_5over2A + x**2 * n_3A))/((1 + d_1A*x**(2.0/3.0) + d_3over2A*Mf*PI))) + NRTuned_dphaseB1=-((c_NewtB*kappaB*dynk2barfunc_deriv*x**(5.0/3.0)*(1 + x**(2.0/3.0)*n_1B + Mf*n_3over2B*PI + x**(4.0/3.0)*n_2B + x**(5.0/3.0)*n_5over2B + x**2 * n_3B))/((1 + d_1B*x**(2.0/3.0) + d_3over2B*Mf*PI))) + + NRTuned_dphaseA2=((c_NewtA*kappaA*dynk2barfunc*Mf**(2.0/3.0) * PI**(5.0/3.0) * (-5 - x**(2.0/3.0)*(3*d_1A + 7*n_1A) - 2*Mf*(d_3over2A + 4*n_3over2A)*PI - x**(4.0/3.0)*(5*d_1A*n_1A + 9*n_2A) - 2*x**(5.0/3.0)*(2*d_3over2A*n_1A + 3*d_1A*n_3over2A + 5*n_5over2A) - x**2*(7*d_1A*n_2A + 11*n_3A + 5*d_3over2A*n_3over2A) - 2*x**(7.0/3.0)*(3*d_3over2A*n_2A + 4*d_1A*n_5over2A) - x**(8.0/3.0)*(9*d_1A*n_3A + 7*d_3over2A*n_5over2A) - 2*x**3 *(4*d_3over2A*n_3A)))/(3.*jnp.pow(1 + d_1A*x**(2.0/3.0) + d_3over2A*Mf*PI, 2))) + NRTuned_dphaseB2=((c_NewtB*kappaB*dynk2barfunc*Mf**(2.0/3.0) * PI**(5.0/3.0) * (-5 - x**(2.0/3.0)*(3*d_1B + 7*n_1B) - 2*Mf*(d_3over2B + 4*n_3over2B)*PI - x**(4.0/3.0)*(5*d_1B*n_1B + 9*n_2B) - 2*x**(5.0/3.0)*(2*d_3over2B*n_1B + 3*d_1B*n_3over2B + 5*n_5over2B) - x**2*(7*d_1B*n_2B + 11*n_3B + 5*d_3over2B*n_3over2B) - 2*x**(7.0/3.0)*(3*d_3over2B*n_2B + 4*d_1B*n_5over2B) - x**(8.0/3.0)*(9*d_1B*n_3B + 7*d_3over2B*n_5over2B) - 2*x**3 *(4*d_3over2B*n_3B)))/(3.*jnp.pow(1 + d_1B*x**(2.0/3.0) + d_3over2B*Mf*PI, 2))) + + factorA = -c_NewtA*kappaA + factorB = -c_NewtB*kappaB + + NRTuned_dphaseNRT = NRTuned_dphaseA1 + NRTuned_dphaseA2 + NRTuned_dphaseB1 + NRTuned_dphaseB2 + + PNpolyA = (1 + x**(2.0/3.0)*c_1A + Mf*c_3over2A*PI + x**(4.0/3.0)*c_2A + x**(5.0/3.0)*c_5over2A) + PNpolyB = (1 + x**(2.0/3.0)*c_1B + Mf*c_3over2B*PI + x**(4.0/3.0)*c_2B + x**(5.0/3.0)*c_5over2B) + + + dPNpolyA = (2./3.)*Mf**(-1.0/3.0)*c_1A*PI**(2.0/3.0) + c_3over2A*PI + (4./3.)*Mf**(1.0/3.0)*c_2A*PI**(4.0/3.0) + (5./3.)*Mf**(2.0/3.0)*c_5over2A*PI**(5.0/3.0) + dPNpolyB = (2./3.)*Mf**(-1.0/3.0)*c_1B*PI**(2.0/3.0) + c_3over2B*PI + (4./3.)*Mf**(1.0/3.0)*c_2B*PI**(4.0/3.0) + (5./3.)*Mf**(2.0/3.0)*c_5over2B*PI**(5.0/3.0) + + PNTuned_dphaseA = factorA*((5./3.)*Mf**(2.0/3.0)*PI**(5.0/3.0)*PNpolyA + x**(5.0/3.0)*dPNpolyA) + PNTuned_dphaseB = factorB*((5./3.)*Mf**(2.0/3.0)*PI**(5.0/3.0)*PNpolyB + x**(5.0/3.0)*dPNpolyB) + + PNTuned_dphase = PNTuned_dphaseA + PNTuned_dphaseB + + Mfmerger = _get_merger_frequency(theta) * M_s + + Mftaperstart = 1.15*Mfmerger + Mftaperend = 1.35*Mfmerger + + + # Eq. (46) in arXiv:2311.07456 */ + plancktaperfn = general_planck_taper(Mf, Mftaperstart, Mftaperend) + dPlancktaper = dGeneral_planck_taper(Mf, Mftaperstart, Mftaperend) * plancktaperfn**2 + + NRTuned_dphase = NRTuned_dphaseNRT*(1.0 - plancktaperfn) + PNTuned_dphase*plancktaperfn - (NRphaseNRT - PNtidalphase)*dPlancktaper + + + dphase+=NRTuned_dphase + + return dphase * M_s + + +def dGeneral_planck_taper(x, y1, y2): + exp_arg = (y2 - y1)/(x - y1) + (y2 - y1)/(x - y2) + exp_fn = jnp.cosh(exp_arg) + jnp.sinh(exp_arg) + dfactor_taper = jnp.where( + x < y1, + 0, + jnp.where( + x > y2, + 0, + -(exp_fn)*(-(y2 - y1)/((x - y1) * (x - y1)) - (y2 - y1)/((x - y2) * (x - y2))), + ), + ) + + return dfactor_taper \ No newline at end of file diff --git a/test/benchmark_waveform.py b/test/benchmark_waveform.py index 35c95314..0176e66d 100644 --- a/test/benchmark_waveform.py +++ b/test/benchmark_waveform.py @@ -8,8 +8,11 @@ import time import numpy as np import jax +jax.config.update("jax_enable_x64", True) + import jax.numpy as jnp import pandas as pd +from ripplegw.waveforms.NRTidalv3_utils import _get_merger_frequency from tqdm import tqdm from ripplegw import get_eff_pads, get_match_arr, ms_to_Mc_eta, lambdas_to_lambda_tildes @@ -18,8 +21,6 @@ import lal import lalsimulation as lalsim -jax.config.update("jax_enable_x64", True) - ########################### ### Auxiliary functions ### ########################### @@ -65,7 +66,7 @@ def waveform(theta): # Import the waveform from ripplegw.waveforms.IMRPhenomXAS import gen_IMRPhenomXAS_hphc as waveform_generator - # Get jitted version (note, use IMRPhenomD as underlying waveform model) + # Get jitted version @jax.jit def waveform(theta): hp, _ = waveform_generator(fs, theta, f_ref) @@ -75,7 +76,7 @@ def waveform(theta): # Import the waveform from ripplegw.waveforms.IMRPhenomXAS_NRTidalv3 import gen_IMRPhenomXAS_NRTidalv3_hphc as waveform_generator - # Get jitted version (note, use IMRPhenomD as underlying waveform model) + # Get jitted version @jax.jit def waveform(theta): hp, _ = waveform_generator(fs, theta, f_ref) @@ -143,6 +144,7 @@ def random_match(n: int, bounds: dict, IMRphenom: str = "IMRPhenomD_NRTidalv2", # Get a frequency domain waveform thetas = [] matches = [] + delta_f_merger = [] import os psd_file = os.path.join(os.path.dirname(__file__), psd_file) @@ -152,22 +154,22 @@ def random_match(n: int, bounds: dict, IMRphenom: str = "IMRPhenomD_NRTidalv2", # Mismatches computations: for _ in tqdm(range(n)): non_precessing_matchmaking( - bounds, IMRphenom, f_l, f_u, df, fs, waveform, f_ASD, ASD, thetas, matches + bounds, IMRphenom, f_l, f_u, df, fs, waveform, f_ASD, ASD, thetas, matches, delta_f_merger ) # Save and report mismatches thetas = np.array(thetas) matches = np.array(matches) if outdir is not None: - csv_name = f"{outdir}/matches_{IMRphenom}_withMIN.csv" + csv_name = f"{outdir}/matches_{IMRphenom}_highMass.csv" print(f"Saving matches to {csv_name}") - df = save_matches(csv_name, thetas, matches, is_tidal=is_tidal, verbose=True) + df = save_matches(csv_name, thetas, matches, delta_f_merger=delta_f_merger, is_tidal=is_tidal, verbose=True) return df def non_precessing_matchmaking( - bounds, IMRphenom, f_l, f_u, df, fs, waveform, f_ASD, ASD, thetas, matches, + bounds, IMRphenom, f_l, f_u, df, fs, waveform, f_ASD, ASD, thetas, matches, delta_f_merger, ): is_tidal = check_is_tidal(IMRphenom) @@ -179,7 +181,7 @@ def non_precessing_matchmaking( l1 = np.random.uniform(bounds["lambda"][0], bounds["lambda"][1]) l2 = np.random.uniform(bounds["lambda"][0], bounds["lambda"][1]) - dist_mpc = np.random.uniform(bounds["d_L"][0], bounds["d_L"][1]) + dist_mpc = 50 #np.random.uniform(bounds["d_L"][0], bounds["d_L"][1]) tc = 0.0 inclination = np.random.uniform(0, 2*PI) phi_ref = np.random.uniform(0, 2*PI) @@ -281,7 +283,21 @@ def non_precessing_matchmaking( ) thetas.append(theta) -def save_matches(filename, thetas, matches, verbose=True, is_tidal=False): + # M = theta[0] + theta[1] + # q = theta[0]/theta[1] + # f_merger_LAL = lalsim.SimNRTunedTidesMergerFrequency_v3( + # M, + # theta[4], + # theta[5], + # q, + # theta[2], + # theta[3] + # ) + # f_merger_ripple = _get_merger_frequency(theta[:6]) + + # delta_f_merger.append(f_merger_ripple - f_merger_LAL) + +def save_matches(filename, thetas, matches, delta_f_merger, verbose=True, is_tidal=False): # Get the parameters, which depends on whether or not tidal: if is_tidal: @@ -309,7 +325,9 @@ def save_matches(filename, thetas, matches, verbose=True, is_tidal=False): 'phi_ref': phi_ref, 'inclination': inclination, 'match': matches, - 'mismatch': mismatches} + 'mismatch': mismatches, + # 'delta_f_merger': delta_f_merger + } else: m1 = thetas[:, 0] m2 = thetas[:, 1] @@ -562,8 +580,8 @@ def lal_waveform_no_tidal(theta): # Showing an example of benchmarking: bounds = {"m": [0.5, 3.0], - "chi": [-0.05, 0.05], - "lambda": [0.0, 5000.0], + "chi": [0, 0.99], + "lambda": [0.0, 25000.0], "d_L": [30.0, 300.0]} approximant = "IMRPhenomXAS_NRTidalv3" From 916179d79ffd555961d2aae75d55231d0fb88894 Mon Sep 17 00:00:00 2001 From: robkamcha Date: Tue, 25 Nov 2025 11:22:42 +0100 Subject: [PATCH 034/474] Cleaned up units and code --- src/ripplegw/waveforms/NRTidalv3_utils.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/src/ripplegw/waveforms/NRTidalv3_utils.py b/src/ripplegw/waveforms/NRTidalv3_utils.py index 3745be28..13c3560f 100644 --- a/src/ripplegw/waveforms/NRTidalv3_utils.py +++ b/src/ripplegw/waveforms/NRTidalv3_utils.py @@ -39,9 +39,7 @@ def _get_merger_frequency(theta: Array): """ m1, m2, chi1, chi2, lambda1, lambda2 = theta M = m1 + m2 - m1_s = m1 * gt - m2_s = m2 * gt - q = m1_s / m2_s + q = m1 / m2 Xa = q/(1.0+q) Xb = 1.0 - Xa @@ -103,7 +101,7 @@ def _get_merger_frequency(theta: Array): # Full tidal correction -def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, P_P: Array): +def fullTidalPhaseCorrection(Mf: Array, theta_intrinsic: Array, P_P: Array): """ Returns the NRTidalv3 phase corrections due to tidal and spin effects. @@ -117,9 +115,8 @@ def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, P_P: Array): """ m1, m2, _, _, lambda1, lambda2 = theta_intrinsic - M_s = (m1 + m2) * gt Xa = m1 / (m1 + m2) - x = PI * f * M_s + x = PI * Mf x_23 = x**(2.0/3.0) PN_coeffs = get_tidalphasePN_coeffs(theta_intrinsic) @@ -133,7 +130,9 @@ def fullTidalPhaseCorrection(f: Array, theta_intrinsic: Array, P_P: Array): def changePhase_if_min(f, NRTidalv3_phase, valid): - idx = jnp.argmax(valid) + idx = jnp.argmax(valid) - 1 + idx = jnp.maximum(idx, 0) # Avoid negative index + tidal_min_value = NRTidalv3_phase[idx] mask = (jnp.arange(f.size) >= idx) return jnp.where(mask, tidal_min_value, NRTidalv3_phase) @@ -185,7 +184,7 @@ def get_tidal_phase( # expression for the effective love number enhancement factor, see Eq. (27) of https://arxiv.org/pdf/2311.07456.pdf.*/ dynk2bar = 1.0 + ((s1) - 1)*(1.0/(1.0 + exps2Mom*exps2s3)) - ((s1-1.0)/(1.0 + exps2s3)) - 2.0*M_omega*((s1) - 1)*s2*exps2s3/((1.0 + exps2s3)*(1.0 + exps2s3)) - PN_x = M_omega**(2.0/3.0) # redundant computation? + PN_x = M_omega**(2.0/3.0) PN_x_2 = PN_x * PN_x PN_x_3 = PN_x * PN_x_2 PN_x_3over2 = PN_x * jnp.sqrt(PN_x) From f41830b4f2101577360960aa416eef59e9d9b8f0 Mon Sep 17 00:00:00 2001 From: Thomas Ng Date: Tue, 25 Nov 2025 11:34:58 +0100 Subject: [PATCH 035/474] Remove JIT compilation decorators from phase and amplitude functions --- src/ripplegw/waveforms/IMRPhenomD.py | 3 --- src/ripplegw/waveforms/IMRPhenomD_utils.py | 1 - src/ripplegw/waveforms/IMRPhenomXAS.py | 2 -- 3 files changed, 6 deletions(-) diff --git a/src/ripplegw/waveforms/IMRPhenomD.py b/src/ripplegw/waveforms/IMRPhenomD.py index efcf84f7..b7ce6a04 100644 --- a/src/ripplegw/waveforms/IMRPhenomD.py +++ b/src/ripplegw/waveforms/IMRPhenomD.py @@ -381,7 +381,6 @@ def get_IIb_Amp(fM_s: Array, theta: Array, coeffs: Array, f_RD, f_damp) -> Array return Amp_IIb -# @jax.jit def Phase(f: Array, theta: Array, coeffs: Array, transition_freqs: Array) -> Array: """ Computes the phase of the PhenomD waveform following 1508.07253. @@ -459,7 +458,6 @@ def Phase(f: Array, theta: Array, coeffs: Array, transition_freqs: Array) -> Arr return phase -# @jax.jit def Amp( f: Array, theta: Array, coeffs: Array, transition_frequencies: Array, D=1 ) -> Array: @@ -514,7 +512,6 @@ def Amp( return Amp0 * Amp * (M_s**2.0) / dist_s -# @jax.jit def _gen_IMRPhenomD( f: Array, theta_intrinsic: Array, diff --git a/src/ripplegw/waveforms/IMRPhenomD_utils.py b/src/ripplegw/waveforms/IMRPhenomD_utils.py index 61a7ab2e..5930be42 100644 --- a/src/ripplegw/waveforms/IMRPhenomD_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomD_utils.py @@ -133,7 +133,6 @@ def get_transition_frequencies( return f1, f2, f3, f4, f_RD, f_damp -@jax.jit def get_coeffs(theta: Array) -> Array: # Retrives the coefficients needed to produce the waveform diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index 9edf1688..fffa2486 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -719,7 +719,6 @@ def get_mergerringdown_raw_phase( return phiRD, (cL, CV_phase_RD0) -# @jax.jit def Phase(f: Array, theta: Array, phase_coeffs: Array) -> Array: """ Computes the phase of the PhenomD waveform following 1508.07253. @@ -1316,7 +1315,6 @@ def Amp(f: Array, theta: Array, amp_coeffs: Array, D=1.0) -> Array: return Overallamp * Amp * (fM_s ** (-7.0 / 6.0)) -# @jax.jit def _gen_IMRPhenomXAS( f: Array, theta_intrinsic: Array, From 63ea8d4f101ca802bbd5b63e658c77f5a35cba02 Mon Sep 17 00:00:00 2001 From: Thomas Ng Date: Wed, 26 Nov 2025 14:14:43 +0100 Subject: [PATCH 036/474] Remove Twitter metadata from MkDocs configuration --- mkdocs.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 51a03967..f8a555d2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -27,9 +27,6 @@ theme: icon: material/brightness-2 name: Light mode - twitter_name: "@physicskaze" - twitter_url: "https://twitter.com/physicskaze" - markdown_extensions: - pymdownx.arithmatex: # Render LaTeX via MathJax generic: true From be965ccb8fcbe1d127fd2f0be51cdea163d6dd47 Mon Sep 17 00:00:00 2001 From: Thomas Ng Date: Wed, 26 Nov 2025 14:17:01 +0100 Subject: [PATCH 037/474] Update Dependabot schedule to run on Mondays --- .github/dependabot.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index f53fb3f8..854f7de6 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -4,7 +4,7 @@ updates: directory: "/" schedule: interval: "weekly" - day: "sunday" + day: "monday" target-branch: "ripple-dev" commit-message: prefix: "ci" From 15f6596636e02f6ee565b5f5c36410c72d2e9b4f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 26 Nov 2025 13:39:41 +0000 Subject: [PATCH 038/474] Bump pypa/gh-action-pypi-publish Bumps the github_actions group with 1 update in the /.github/workflows directory: [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish). Updates `pypa/gh-action-pypi-publish` from 1.8.4 to 1.13.0 - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.8.4...v1.13.0) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-version: 1.13.0 dependency-type: direct:production dependency-group: github_actions ... Signed-off-by: dependabot[bot] --- .github/workflows/python-publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 30f2bc2f..ede3daec 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -33,7 +33,7 @@ jobs: - name: Build package run: python -m build - name: Publish package - uses: pypa/gh-action-pypi-publish@v1.8.4 + uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} From c17082b32a25ba614fb7b829252dff4804491e7b Mon Sep 17 00:00:00 2001 From: Thomas Ng Date: Thu, 27 Nov 2025 14:27:50 +0100 Subject: [PATCH 039/474] Standardise repo structure across repos --- .github/dependabot.yml | 9 + .github/workflows/CI.yml | 64 + .github/workflows/python-publish.yml | 39 - .pre-commit-config.yaml | 14 + pyproject.toml | 87 +- setup.cfg | 30 - setup.py | 8 - src/ripplegw/__init__.py | 3 +- src/ripplegw/noise.py | 2 +- src/ripplegw/typing.py | 11 - src/ripplegw/waveforms/IMRPhenomD.py | 2 +- .../waveforms/IMRPhenomD_NRTidalv2.py | 2 +- src/ripplegw/waveforms/IMRPhenomD_QNMdata.py | 4 +- src/ripplegw/waveforms/IMRPhenomD_utils.py | 2 +- src/ripplegw/waveforms/IMRPhenomPv2.py | 14 +- src/ripplegw/waveforms/IMRPhenomPv2_utils.py | 11 +- src/ripplegw/waveforms/IMRPhenomXAS.py | 14 +- src/ripplegw/waveforms/IMRPhenomX_utils.py | 4 - .../waveforms/IMRPhenom_tidal_utils.py | 2 +- src/ripplegw/waveforms/SineGaussian.py | 2 +- src/ripplegw/waveforms/TaylorF2.py | 8 +- uv.lock | 2458 +++++++++++++++++ 22 files changed, 2658 insertions(+), 132 deletions(-) create mode 100644 .github/workflows/CI.yml delete mode 100644 .github/workflows/python-publish.yml create mode 100644 .pre-commit-config.yaml delete mode 100644 setup.cfg delete mode 100644 setup.py delete mode 100644 src/ripplegw/typing.py create mode 100644 uv.lock diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 854f7de6..caeb123c 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -8,3 +8,12 @@ updates: target-branch: "ripple-dev" commit-message: prefix: "ci" + + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" + day: "monday" + target-branch: "ripple-dev" + commit-message: + prefix: "deps" diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml new file mode 100644 index 00000000..1cd61482 --- /dev/null +++ b/.github/workflows/CI.yml @@ -0,0 +1,64 @@ +name: CI + +on: + pull_request: + branches: [main, ripple-dev] + push: + branches: [main, ripple-dev] + release: + types: [published] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.11", "3.12", "3.13"] + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + - name: Set up Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + - name: Install dependencies + run: uv sync --all-extras --dev + - name: Run tests + run: uv run pytest --cov=ripplegw --cov-report=term-missing test/ + - name: Upload coverage to Coveralls + if: matrix.python-version == '3.12' + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }} + run: uv run coveralls + + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + - name: Set up Python + run: uv python install 3.12 + - name: Install dependencies + run: uv sync --all-extras --dev + - name: Run pre-commit + run: uv run pre-commit run --all-files + + publish: + needs: [test, pre-commit] + runs-on: ubuntu-latest + if: github.event_name == 'release' + environment: + name: pypi + url: https://pypi.org/project/ripplegw/ + permissions: + id-token: write + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + - name: Build package + run: uv build + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml deleted file mode 100644 index ede3daec..00000000 --- a/.github/workflows/python-publish.yml +++ /dev/null @@ -1,39 +0,0 @@ -# This workflow will upload a Python Package using Twine when a release is created -# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries - -# This workflow uses actions that are not certified by GitHub. -# They are provided by a third-party and are governed by -# separate terms of service, privacy policy, and support -# documentation. - -name: Upload Python Package - -on: - release: - types: [published] - -permissions: - contents: read - -jobs: - deploy: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v3 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install build - - name: Build package - run: python -m build - - name: Publish package - uses: pypa/gh-action-pypi-publish@v1.13.0 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..737d8f79 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,14 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.2 + hooks: + - id: ruff-check + args: ["--fix"] + - id: ruff-format + + # Temporarily disabled - re-enable once type errors are fixed + # - repo: https://github.com/RobertCraigie/pyright-python + # rev: v1.1.402 + # hooks: + # - id: pyright + # additional_dependencies: [jax, jaxtyping, pytest, typing_extensions, numpy] diff --git a/pyproject.toml b/pyproject.toml index 374b58cb..5546a033 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,85 @@ [build-system] -requires = [ - "setuptools>=42", - "wheel" +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "ripplegw" +version = "0.0.9" +description = "A small jax package for differentiable and fast gravitational wave data analysis." +readme = "README.md" +requires-python = ">=3.11" +authors = [ + { name = "Thomas Edwards", email = "tedwards2412@gmail.com" }, +] +license = { file = "LICENSE" } +keywords = ["gravitational waves", "jax"] +classifiers = [ + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] +dependencies = [ + "jax>=0.5.0", + "jaxlib>=0.5.0", + "jaxtyping>=0.2.34", + "numpy>=1,<2", +] + +[project.urls] +Documentation = "https://ripple.readthedocs.io/en/latest/" +"Bug Tracker" = "https://github.com/tedwards2412/ripple" + +[project.optional-dependencies] +docs = [ + "mkdocs-gen-files==0.5.0", + "mkdocs-jupyter==0.25.1", + "mkdocs-literate-nav==0.6.1", + "mkdocs-material==9.5.47", + "mkdocs==1.6.1", + "mkdocstrings[python]==0.27.0", + "pymdown-extensions==10.12", +] +cuda = [ + "jax[cuda12]>=0.5.0", +] + +[tool.uv] +dev-dependencies = [ + "ripplegw", + "pre-commit>=4.2.0", + "coverage>=7.7.0", + "coveralls>=4.0.1", + "pytest>=8.3.3", + "pytest-cov>=6.0.0", + "ruff>=0.12.2", + # "pyright>=1.1.402", # Temporarily disabled - re-enable once type errors are fixed +] + +[tool.uv.sources] +ripplegw = { workspace = true } + +[tool.ruff] +include = ["src"] +exclude = ["test", "docs", "notebooks"] + +[tool.ruff.lint] +ignore = [ + "F722", # Forward annotations evaluation (false positive with jaxtyping) + "E731", # Lambda assignment +] + +[tool.ruff.lint.extend-per-file-ignores] +"notebooks/*" = ["E402"] + +[tool.pyright] +include = ["src"] +exclude = ["docs", "test"] + +[tool.coverage.report] +exclude_also = [ + 'def __repr__', + "raise AssertionError", + "raise NotImplementedError", + "@(abc\\. )?abstractmethod", ] -build-backend = "setuptools.build_meta" diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index a29af57f..00000000 --- a/setup.cfg +++ /dev/null @@ -1,30 +0,0 @@ -[metadata] -name = ripplegw -version = 0.0.9 -description = A small jax package for differentiable and fast gravitational wave data analysis. -long_description = file: README.md -long_description_content_type = text/markdown -keywords = gravitational waves, jax -url = https://ripple.readthedocs.io/en/latest/ -project_urls = - Bug Tracker = https://github.com/tedwards2412/ripple -classifiers = - Programming Language :: Python :: 3 - License :: OSI Approved :: MIT License - Operating System :: OS Independent - -[options] -package_dir = - = src -packages = find: -python_requires = >=3.9 -install_requires = - jax - jaxlib - numpy>=1,<2 - -[options.package_data] -* = *.dat - -[options.packages.find] -where = src diff --git a/setup.py b/setup.py deleted file mode 100644 index 7772c915..00000000 --- a/setup.py +++ /dev/null @@ -1,8 +0,0 @@ -from setuptools import setup - -setup( - name="ripplegw", - install_requires=[ - "ripplegw", - ], -) diff --git a/src/ripplegw/__init__.py b/src/ripplegw/__init__.py index f80bbd62..fd908433 100644 --- a/src/ripplegw/__init__.py +++ b/src/ripplegw/__init__.py @@ -7,7 +7,6 @@ # from jax.config import config # config.update("jax_enable_x64", True) -import ripplegw from math import pi from typing import Callable, Optional, Tuple import warnings @@ -16,7 +15,7 @@ import jax.numpy as jnp from .constants import C, G -from .typing import Array +from jaxtyping import Array def Mc_eta_to_ms(m): diff --git a/src/ripplegw/noise.py b/src/ripplegw/noise.py index 3e883fcf..f14f3977 100644 --- a/src/ripplegw/noise.py +++ b/src/ripplegw/noise.py @@ -10,7 +10,7 @@ import numpy as np from . import noise_resources -from .typing import Array +from jaxtyping import Array f_range_LIGOI = (40.0, 1e4) diff --git a/src/ripplegw/typing.py b/src/ripplegw/typing.py deleted file mode 100644 index 785a70b7..00000000 --- a/src/ripplegw/typing.py +++ /dev/null @@ -1,11 +0,0 @@ -""" -Typing definitions to be shared across files. -""" - -import jax -import jax.numpy as jnp - - -# TODO: what type should this be? -# PRNGKeyArray = jax.random.PRNGKeyArray # type: ignore -Array = jnp.ndarray diff --git a/src/ripplegw/waveforms/IMRPhenomD.py b/src/ripplegw/waveforms/IMRPhenomD.py index b7ce6a04..ec56c29b 100644 --- a/src/ripplegw/waveforms/IMRPhenomD.py +++ b/src/ripplegw/waveforms/IMRPhenomD.py @@ -14,7 +14,7 @@ from .IMRPhenomD_QNMdata import fM_CUT from ..constants import EulerGamma, gt, m_per_Mpc, C, PI -from ..typing import Array +from jaxtyping import Array from ripplegw import Mc_eta_to_ms diff --git a/src/ripplegw/waveforms/IMRPhenomD_NRTidalv2.py b/src/ripplegw/waveforms/IMRPhenomD_NRTidalv2.py index 1f451803..bdfdfde0 100644 --- a/src/ripplegw/waveforms/IMRPhenomD_NRTidalv2.py +++ b/src/ripplegw/waveforms/IMRPhenomD_NRTidalv2.py @@ -5,7 +5,7 @@ import jax import jax.numpy as jnp from ..constants import gt, m_per_Mpc, PI, TWO_PI, MRSUN -from ..typing import Array +from jaxtyping import Array from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas from .IMRPhenom_tidal_utils import get_quadparam_octparam, get_kappa from ripplegw.waveforms.IMRPhenomD import Phase, Amp, get_IIb_raw_phase diff --git a/src/ripplegw/waveforms/IMRPhenomD_QNMdata.py b/src/ripplegw/waveforms/IMRPhenomD_QNMdata.py index 2b06b0eb..bbde5d4a 100644 --- a/src/ripplegw/waveforms/IMRPhenomD_QNMdata.py +++ b/src/ripplegw/waveforms/IMRPhenomD_QNMdata.py @@ -1,4 +1,5 @@ import jax.numpy as jnp +from scipy.interpolate import CubicSpline fM_CUT = 0.2 @@ -3027,9 +3028,6 @@ ] ) - -from scipy.interpolate import CubicSpline - QNMData_a = jnp.linspace(-1, 1, 500_000) QNMData_fRD = CubicSpline(_QNMData_a, _QNMData_fRD)(QNMData_a) QNMData_fdamp = CubicSpline(_QNMData_a, _QNMData_fdamp)(QNMData_a) diff --git a/src/ripplegw/waveforms/IMRPhenomD_utils.py b/src/ripplegw/waveforms/IMRPhenomD_utils.py index 5930be42..457461d2 100644 --- a/src/ripplegw/waveforms/IMRPhenomD_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomD_utils.py @@ -4,7 +4,7 @@ import jax from ..constants import gt -from ..typing import Array +from jaxtyping import Array from .IMRPhenomD_QNMdata import QNMData_a, QNMData_fRD, QNMData_fdamp diff --git a/src/ripplegw/waveforms/IMRPhenomPv2.py b/src/ripplegw/waveforms/IMRPhenomPv2.py index d103328f..6c4ddb2e 100644 --- a/src/ripplegw/waveforms/IMRPhenomPv2.py +++ b/src/ripplegw/waveforms/IMRPhenomPv2.py @@ -2,15 +2,19 @@ import jax.numpy as jnp from ripplegw import Mc_eta_to_ms -from ..constants import gt, MSUN -import numpy as np +from ..constants import gt from .IMRPhenomD import Phase as PhDPhase from .IMRPhenomD import Amp as PhDAmp from .IMRPhenomD_utils import get_coeffs -from ..typing import Array -from .IMRPhenomPv2_utils import * -from .IMRPhenomD_utils import * +from jaxtyping import Array +from .IMRPhenomPv2_utils import ( + WignerdCoefficients, + convert_spins, + ComputeNNLOanglecoeffs, + SpinWeightedY, + phP_get_transition_frequencies, +) def PhenomPCoreTwistUp( diff --git a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py index 5aa51b28..42a9e988 100644 --- a/src/ripplegw/waveforms/IMRPhenomPv2_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomPv2_utils.py @@ -1,19 +1,14 @@ import jax import jax.numpy as jnp -from ripplegw import Mc_eta_to_ms from typing import Tuple -from ..constants import gt, MSUN +from ..constants import gt import numpy as np -from .IMRPhenomD import Phase as PhDPhase -from .IMRPhenomD import Amp as PhDAmp from .IMRPhenomD_utils import ( - get_coeffs, - get_transition_frequencies, EradRational0815, FinalSpin0815_s, ) -from ..typing import Array +from jaxtyping import Array from .IMRPhenomD_QNMdata import QNMData_a, QNMData_fRD, QNMData_fdamp MAX_TOL_ATAN = 1.0e-15 @@ -177,7 +172,7 @@ def convert_spins( return chi1_l, chi2_l, chip, thetaJN, alpha0, phi_aligned, zeta_polariz -def SpinWeightedY(theta, phi, s, l, m): +def SpinWeightedY(theta, phi, s, l, m): # noqa: E741 "copied from SphericalHarmonics.c in LAL" if s == -2: if l == 2: diff --git a/src/ripplegw/waveforms/IMRPhenomXAS.py b/src/ripplegw/waveforms/IMRPhenomXAS.py index fffa2486..fd4bd78c 100644 --- a/src/ripplegw/waveforms/IMRPhenomXAS.py +++ b/src/ripplegw/waveforms/IMRPhenomXAS.py @@ -3,7 +3,7 @@ import jax.numpy as jnp from ..constants import EulerGamma, gt, m_per_Mpc, C, PI from ripplegw.waveforms import IMRPhenomX_utils -from ..typing import Array +from jaxtyping import Array from ripplegw import Mc_eta_to_ms @@ -68,9 +68,7 @@ def get_inspiral_phase(fM_s: Array, theta: Array, phase_coeffs: Array) -> Array: ) ) / 16.0 - ) * PI ** ( - 4.0 / 3.0 - ) + ) * PI ** (4.0 / 3.0) phi5 = 0.0 phi5L = ((5.0 * (46374.0 - 6552.0 * eta) * PI) / 4536.0) * PI ** (5.0 / 3.0) + ( ( @@ -542,7 +540,7 @@ def get_intermediate_raw_phase( - b2 * (fM_s**-1.0) - b3 * (fM_s**-2.0) / 2.0 - (b4 * (fM_s**-3.0) / 3.0) - + (2.0 * cL * jnp.arctan(((fM_s - fMs_RD)) / (2.0 * fMs_damp))) / fMs_damp + + (2.0 * cL * jnp.arctan((fM_s - fMs_RD) / (2.0 * fMs_damp))) / fMs_damp ) @@ -557,8 +555,8 @@ def get_mergerringdown_raw_phase( delta = jnp.sqrt(1.0 - 4.0 * eta) mm1 = 0.5 * (1.0 + delta) mm2 = 0.5 * (1.0 - delta) - chi_eff = mm1 * chi1 + mm2 * chi2 - S = (chi_eff - (38.0 / 113.0) * eta * (chi1 + chi2)) / (1.0 - (76.0 * eta / 113.0)) + # chi_eff = mm1 * chi1 + mm2 * chi2 + # S = (chi_eff - (38.0 / 113.0) * eta * (chi1 + chi2)) / (1.0 - (76.0 * eta / 113.0)) chia = chi1 - chi2 StotR = (mm1**2 * chi1 + mm2**2 * chi2) / (mm1**2 + mm2**2) @@ -1016,7 +1014,7 @@ def get_intermediate_Amp( m2_s = m2 * gt M_s = m1_s + m2_s eta = m1_s * m2_s / (M_s**2.0) - eta2 = eta * eta + # eta2 = eta * eta delta = jnp.sqrt(1.0 - 4.0 * eta) mm1 = 0.5 * (1.0 + delta) diff --git a/src/ripplegw/waveforms/IMRPhenomX_utils.py b/src/ripplegw/waveforms/IMRPhenomX_utils.py index a8f8c48c..12bb03b7 100644 --- a/src/ripplegw/waveforms/IMRPhenomX_utils.py +++ b/src/ripplegw/waveforms/IMRPhenomX_utils.py @@ -1,10 +1,6 @@ -from typing import Tuple - import jax.numpy as jnp -import jax from ..constants import gt, PI -from ..typing import Array # Dimensionless cutoff frequency for PhenomXAS fM_CUT = 0.3 diff --git a/src/ripplegw/waveforms/IMRPhenom_tidal_utils.py b/src/ripplegw/waveforms/IMRPhenom_tidal_utils.py index 56ffb203..430a6c93 100644 --- a/src/ripplegw/waveforms/IMRPhenom_tidal_utils.py +++ b/src/ripplegw/waveforms/IMRPhenom_tidal_utils.py @@ -4,7 +4,7 @@ import jax import jax.numpy as jnp -from ..typing import Array +from jaxtyping import Array def universal_relation(coeffs: Array, x: float): diff --git a/src/ripplegw/waveforms/SineGaussian.py b/src/ripplegw/waveforms/SineGaussian.py index 74d55605..d4dc1b9d 100644 --- a/src/ripplegw/waveforms/SineGaussian.py +++ b/src/ripplegw/waveforms/SineGaussian.py @@ -2,7 +2,7 @@ from jax.lax import complex from ..constants import PI -from ..typing import Array +from jaxtyping import Array def semi_major_minor_from_e(e: Array) -> tuple[Array, Array]: diff --git a/src/ripplegw/waveforms/TaylorF2.py b/src/ripplegw/waveforms/TaylorF2.py index f2956f37..56f9f9f4 100644 --- a/src/ripplegw/waveforms/TaylorF2.py +++ b/src/ripplegw/waveforms/TaylorF2.py @@ -4,7 +4,7 @@ import jax.numpy as jnp from ..constants import EulerGamma, gt, m_per_Mpc, PI, MRSUN -from ..typing import Array +from jaxtyping import Array from ripplegw import Mc_eta_to_ms, lambda_tildes_to_lambdas from .IMRPhenom_tidal_utils import get_quadparam_octparam @@ -368,14 +368,14 @@ def _gen_TaylorF2( dist_mpc, tc, phi_ref = theta_extrinsic m1_s = m1 * gt m2_s = m2 * gt - M = m1 + m2 + # M = m1 + m2 M_s = (m1 + m2) * gt eta = m1_s * m2_s / (M_s**2.0) piM = PI * M_s # TODO: incorporate this into the waveform - vISCO = 1.0 / jnp.sqrt(6.0) - fISCO = vISCO * vISCO * vISCO / piM + # vISCO = 1.0 / jnp.sqrt(6.0) + # fISCO = vISCO * vISCO * vISCO 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b/.github/workflows/CI.yml @@ -24,7 +24,7 @@ jobs: - name: Install dependencies run: uv sync --all-extras --dev - name: Run tests - run: uv run pytest --cov=ripplegw --cov-report=term-missing test/ + run: uv run pytest --cov=ripplegw --cov-report=term-missing test/ --ignore=test/old_tests - name: Upload coverage to Coveralls if: matrix.python-version == '3.12' env: diff --git a/.gitignore b/.gitignore index bd61cd2c..a5c954e5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,22 @@ .DS_Store .ipynb_checkpoints/ .python-version -*__pycache__ +__pycache__/ *.egg-info docs/build/* figures/*.pdf .vscode/* -build/* \ No newline at end of file +# Coverage +.coverage +.coverage.* +htmlcov/ +.pytest_cache/ + +# Build +build/* +dist/ + +# Virtual environments +.venv/ +venv/ \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 5546a033..5b7ab012 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,8 +44,8 @@ cuda = [ "jax[cuda12]>=0.5.0", ] -[tool.uv] -dev-dependencies = [ +[dependency-groups] +dev = [ "ripplegw", "pre-commit>=4.2.0", "coverage>=7.7.0", @@ -53,6 +53,7 @@ dev-dependencies = [ "pytest>=8.3.3", "pytest-cov>=6.0.0", "ruff>=0.12.2", + "tqdm>=4.66.0", # "pyright>=1.1.402", # Temporarily disabled - re-enable once type errors are fixed ] diff --git a/test/test_conversion.py b/test/test_conversion.py index 7910b88b..5e633dd0 100644 --- a/test/test_conversion.py +++ b/test/test_conversion.py @@ -1,4 +1,4 @@ -from ripple import lambdas_to_lambda_tildes, lambda_tildes_to_lambdas +from ripplegw import lambdas_to_lambda_tildes, lambda_tildes_to_lambdas import jax.numpy as jnp import numpy as np diff --git a/uv.lock b/uv.lock index 6010fdd2..a0466222 100644 --- a/uv.lock +++ b/uv.lock @@ -2015,6 +2015,7 @@ dev = [ { name = "pytest-cov" }, { name = "ripplegw" }, { name = "ruff" }, + { name = "tqdm" }, ] [package.metadata] @@ -2043,6 +2044,7 @@ dev = [ { name = "pytest-cov", specifier = ">=6.0.0" }, { name = "ripplegw", editable = 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